#Quantifier Variance

[omegaphilosophia]

The Philosophy of Universal and Existential Quantifiers

The philosophy of universal quantifiers and existential quantifiers deals with how these logical tools are used to express general and particular statements, respectively, and the implications of their use in formal logic, mathematics, and philosophy. These quantifiers are fundamental in the study of logic, helping to clarify the structure of arguments, the nature of truth, and the meaning of propositions.

Key Concepts in the Philosophy of Quantifiers:

Universal Quantifier (∀):

Definition: The universal quantifier, denoted by ∀, is used to express that a statement is true for all members of a particular domain. For example, the statement "∀x (P(x))" means "For all x, P(x) is true."

Example: In a mathematical context, "∀x (x > 0 → x² > 0)" means "For all x, if x is greater than 0, then x squared is greater than 0."

Philosophical Implications: The use of the universal quantifier is central to discussions about generality and the nature of universal truths. Philosophers debate whether statements involving universal quantifiers reflect objective truths about the world or are simply linguistic conventions.

Existential Quantifier (∃):

Definition: The existential quantifier, denoted by ∃, is used to express that there is at least one member of a particular domain for which a statement is true. For example, "∃x (P(x))" means "There exists at least one x such that P(x) is true."

Example: In mathematics, "∃x (x² = 4)" means "There exists an x such that x squared equals 4," which would be true for x = 2 and x = -2.

Philosophical Implications: The existential quantifier raises questions about existence and the ontological commitments of statements. When we say "There exists," what kind of existence are we affirming? This leads to discussions about the nature of existence in various domains (e.g., mathematical, physical, abstract).

Scope and Binding:

Scope: The scope of a quantifier is the part of the statement to which the quantifier applies. Understanding the scope is crucial in determining the meaning of logical expressions.

Binding Variables: Quantifiers bind variables, meaning they specify the domain over which the variable ranges. A variable within the scope of a quantifier is considered bound by that quantifier, whereas a variable outside its scope is free.

Quantifiers in Formal Logic:

Predicate Logic: Universal and existential quantifiers are central to first-order predicate logic, where they allow for the formulation of complex statements about properties and relations. Predicate logic extends propositional logic by including quantifiers and variables that can represent objects in a domain.

Truth Conditions: The truth conditions of statements involving quantifiers depend on the domain of discourse. A universally quantified statement is true if the predicate holds for every element in the domain, while an existentially quantified statement is true if the predicate holds for at least one element.

Philosophical Debates:

Quantifier Variance: Some philosophers, like Hilary Putnam, have argued for "quantifier variance," the idea that the meaning of quantifiers can vary depending on the context, and that different ontological commitments can lead to different interpretations of what exists.

Ontological Commitment: The use of quantifiers in logical expressions often implies certain ontological commitments. For example, stating "∃x (P(x))" suggests a commitment to the existence of at least one object in the domain that satisfies P(x). Philosophers debate whether such commitments are necessary or merely linguistic conventions.

Free Logic: In response to issues of ontological commitment, some logicians have developed "free logic," which allows for the use of quantifiers without assuming the existence of the objects they quantify over. This is particularly relevant in discussions about non-existent or hypothetical entities.

Applications in Philosophy:

Philosophy of Language: In the philosophy of language, quantifiers play a crucial role in understanding meaning, reference, and truth. Debates about the semantics of natural language often involve how universal and existential quantifiers are used in everyday speech.

Metaphysics: Quantifiers are central to metaphysical debates about universals, particulars, and the nature of existence. For instance, discussions about whether universal statements (like "All humans are mortal") are necessarily true, and what that says about the nature of humanity and mortality.

Epistemology: In epistemology, quantifiers are involved in formulating theories of knowledge. For example, when discussing the extent of human knowledge, philosophers might use universal quantifiers to express general knowledge claims or existential quantifiers to assert the existence of specific knowledge.

The philosophy of universal and existential quantifiers is foundational to logic, mathematics, and philosophical inquiry. These quantifiers allow us to express general and particular statements, clarify the structure of arguments, and explore deep questions about existence, truth, and meaning. Their proper use and interpretation are crucial to understanding not only formal systems but also the nature of reality and our linguistic practices.

[sonic-the-hedgehog-2006]

GUN's decryption unit has presence in Soleanna, with the head of the unit, Frances, seeking to enlist local analysts in the military. After stating the "thinking work" is probably not Shadow's cup of tea given his apparent disposition for action-heavy missions, Shadow desires to "prove her wrong" by applying for the division.

After successfully completing the logic puzzles that follow, Frances begs for Shadow to join her team as with the tests results she estimates his IQ to be "about 200."

In another town mission, a mathematician named Alfano claims to have "a higher IQ than the famous Einstein" and regards Shadow as someone who's "always using muscle, and not enough brain." When Shadow completes his set of mathematics puzzles, Alfano becomes surprised and admits Shadow's brain is shockingly impressive. This makes sense, as scholarly sources tend to estimate Einstein's IQ as ranging between 140-160 (though he never took an official test, despite having been alive during their standardization). This humorously creates a range in which Shadow's IQ could be even higher than Alfano's, assuming Alfano's IQ is not many deviations higher than the high-average for Einstein.

Despite a proven accuracy on an individual level with low variance even in decade long control groups, IQ is often questioned as a quantifiable measure of "general intelligence." It is a score meant to measure relative understanding of academia and not definitive understanding, and does not measure broader forms of intelligence such as sociability and adaptiveness to a shifting median—things Shadow has been shown to struggle with, an example being his often portrayed lack of proficiency with modern computers (as in The Murder of Sonic the Hedgehog and, to a lesser extent, Shadow the Hedgehog (2005)). That said, estimated proficiency in academia is regularly proven accurate based on IQ scoring above the median curve.

Shadow's IQ is over 6.5 deviations above the mean—a category often unnamed due to its rarity (though categorization is, again, often broad and inaccurate), and being within the 0.03 percentile (meaning Shadow's IQ range would be shared only by an estimated 2.4 million people on Earth). Worth noting is that—despite what could be assumed—medical studies show past instances of isolated memory loss don't affect a person's intelligence, general knowledge, awareness or attention span, but on occasion PTSD has been shown to cause decline in those categories listed (with no evidence of variability by the severity of trauma experienced).

The number for graduates specifically is difficult to find from scholarly sources, but students pursuing a PhD have an average IQ of 125. That said, and again, Shadow would still likely lack the general skills required to acquire such a degree in modern society.

[covid-safer-hotties]

Also preserved in our archive

By Dr. Sushama R. Chaphalkar, PhD.

In a recent research paper posted to the bioRxiv preprint* server, researchers in the United States investigated the potential effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on cholesterol metabolism, focusing on the role of the viral protein open reading frame 3a (ORF3a).

They found that SARS-CoV-2 causes cholesterol sequestration in lysosomes via the ORF3a protein, which disrupts protein trafficking and reduces the levels of bis(monoacylglycero)phosphate (BMP) in the cell, enhancing viral survival.

Background Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, disrupts lipid metabolism, particularly cholesterol homeostasis, which can persist during and after infection. This is linked to disease severity and long-term complications like dyslipidemia and cardiovascular diseases.

Cholesterol is crucial for cellular function and is primarily transported through lysosomes, where proteins like Niemann-Pick C1 and C2 (NPC1 and NPC2) facilitate its release. SARS-CoV-2 may exploit plasma membrane cholesterol to enhance infectivity.

Disruptions in the lysosomal cholesterol pathway can cause cholesterol buildup, impairing cellular functions, and viruses like Ebola are known to hijack this mechanism. Notably, BMP plays a dual role: it aids in cholesterol transport and contributes to viral infection by promoting viral fusion with lysosomal membranes.

In the present study, researchers investigated the potential impact of SARS-CoV-2 infection on cholesterol transport in cells, focusing on the role of the viral protein ORF3a.

About the Study A variety of experimental techniques were employed, including culturing A549, HeLa, and Vero E6 cells, followed by SARS-CoV-2 infection at different multiplicities of infection. SARS-CoV-2 ORF3a-VPS39 interaction was studied using mutations at key residues (notably W193 and Y184, which were identified as critical for this interaction). Immunofluorescence, filipin staining, and confocal microscopy were used to assess cholesterol localization and vesicular dynamics, while high-content imaging quantified cell-specific responses.

Cholesterol levels were measured using gas chromatography-mass spectrometry (GC-MS), and lipid species were analyzed through shotgun lipidomics. For further protein analysis, western blotting was performed to detect secreted NPC2 and cathepsin D, along with cell lysates. Data were analyzed using ImageJ and Prism 9, and statistical significance was determined by t-tests or analysis of variance.

Results and Discussion SARS-CoV-2 infection was found to increase filipin-positive puncta in lysosomes of A549-hACE2 and Vero E6 cells, indicating altered cholesterol distribution, especially in lysosomes, without affecting total cholesterol levels. Among the 28 viral proteins tested, ORF3a showed the strongest increase in filipin puncta, suggesting significant lysosomal cholesterol sequestration.

Notably, SARS-CoV-2 ORF3a localized to lysosomes and caused them to swell, whereas SARS-CoV ORF3a did not induce such effects, highlighting a distinct pathogenic strategy unique to SARS-CoV-2.

ORF3a was found to interact with VPS39, a key component of the HOPS complex involved in cholesterol egress from lysosomes. Key residues W193 and Y184 were shown to form a hydrophobic binding interface critical for this interaction, distinguishing SARS-CoV-2 ORF3a from its SARS-CoV counterpart. Mutations at W193 and Y184 disrupted this interaction, while S171 and H182 had no significant effect.

SARS-CoV-2 ORF3a expression was shown to cause cholesterol accumulation in lysosomes, which was reduced by the W193A mutation. It also led to the mislocalization of NPC2 and increased its secretion, indicating disrupted NPC2 trafficking, likely due to interference with TGN-to-endosome transport. Additionally, BMP levels were significantly reduced in infected cells, which likely exacerbates lysosomal cholesterol sequestration.

In SARS-CoV-2-infected Vero E6 cells, BMP levels were found to decrease at 12 hours post-infection, coinciding with increased cholesterol at 18 hours. In HeLa-Flp-In cells, SARS-CoV-2 ORF3a was found to reduce BMP levels by 20%, with partial rescue in the W193A mutant. Lipidomics confirmed this reduction, correlating BMP loss with cholesterol accumulation and suggesting BMP reduction may contribute to cholesterol sequestration.

SARS-CoV-2 may reduce plasma membrane cholesterol to limit secondary infections, as shown by decreased SARS-CoV-2 infection in NPC1 inhibitor-treated cells. This supports the hypothesis that the virus manipulates cholesterol distribution to optimize replication conditions. Interestingly, the virus also appears to reduce its own infectivity within a single cell, suggesting a self-regulating mechanism to prevent viral overload and ensure broader host-level spread.

Conclusion In conclusion, a novel mechanism by which SARS-CoV-2 disrupts host cell lipid metabolism, specifically through cholesterol sequestration in lysosomes, has been elucidated. By uncovering the specific interaction between the viral protein ORF3a and host protein VPS39, the study highlights a critical role of lysosomal cholesterol trafficking disruption in SARS-CoV-2 pathogenesis.

This discovery opens potential therapeutic avenues to target lipid dysregulation in COVID-19, which could help mitigate both the disease's immediate and long-term metabolic consequences, including dyslipidemia and cardiovascular complications.

Journal reference: Preliminary scientific report. Manipulation of Host Cholesterol by SARS-CoV-2. Aliza Doyle et al., bioRxiv, 2024.11.13.623299 (2024), DOI: 10.1101/2024.11.13.623299,

Study Link: www.biorxiv.org/content/10.1101/2024.11.13.623299v1

[xyymath]

📊 The Mathematics of Understanding Society: Statistics in Social Sciences

1. Reliability: Quantifying Consistency

Reliability ensures that statistical results are consistent across time and methods. It is measured through techniques like:

Test-Retest Reliability: Same participants, repeated measures.

Inter-Rater Reliability: Agreement between multiple observers.

Internal Consistency: Correlation of test items, often measured using Cronbach’s Alpha.

Formula for internal consistency:

where N is the number of items, cbar is the average covariance between item pairs, and v is the total item variance.

2. Validity: Ensuring Relevance

Validity measures whether data reflects the intended concept. Types include:

Construct Validity: Evaluates how well a test aligns with theoretical concepts.

Criterion Validity: Measures correlation with related, independent outcomes.

Content Validity: Assesses if the test covers the full scope of the concept.

3. Sampling Theory: Representing Populations

In statistics, sampling bridges finite data and infinite populations. Randomized methods minimize bias, while stratified or cluster sampling improves efficiency. The Central Limit Theorem (CLT) guarantees that sampling distributions approximate normality for large sample sizes.

where SE is the standard error, sigma is the population standard deviation, and n is the sample size

4. Minimizing Bias

Bias skews results, reducing reliability and validity. Statistical techniques such as blind sampling, control groups, and adjustments for confounders mitigate these effects. Weighted averages or regression adjustments help correct sampling bias.

5. Significance Testing: Inference in Social Sciences

Statistical tests like t-tests and ANOVA assess relationships in data. P-values determine significance, while effect sizes (e.g., Cohen’s dbar) quantify practical importance.

Example: For comparing group means, the test statistic t is:

where Xbar is the sample mean, s^2 the variance, and n the sample size.

6. Predictive Modeling

Social scientists employ regression models for predictions, such as linear regression:

where β0 is the intercept, β1 the slope, and ϵ the error term.

7. Ethics and Transparency

Statistical transparency is non-negotiable. Misinterpretation or manipulation (e.g., p-hacking) compromises the integrity of findings. Open data and replication strengthen credibility.

"It's easy to lie with statistics. It's hard to tell the truth without statistics"  Darrell Huff

References : (and further reading material)

source one

source two

source three

[spacetimewithstuartgary]

Satellite galaxies gone awry: Andromeda's asymmetrical companions challenge cosmology

The Andromeda galaxy is surrounded by a constellation of dwarf galaxies that are arranged in a highly lopsided manner. Analysis of cosmological simulations published in Nature Astronomy reveal that this degree of asymmetry is only found in 0.3% of similar systems, painting Andromeda as a striking outlier in the current cosmological paradigm.

The spatial distribution of galaxies provides crucial insights into cosmology and dark matter physics. According to the standard cosmological model, small galaxies merge over time in a chaotic process to form larger ones, leaving behind swarms of faint dwarf galaxies that orbit massive host galaxies in an almost random arrangement.

But new research at the Leibniz Institute for Astrophysics Potsdam (AIP) shows that the satellite galaxies of the neighboring Andromeda galaxy (M31) have surprising and thus far unexplained properties.

Instead of being randomly spread around their host galaxy, as the standard model of cosmology predicts, over 80% of these dwarf galaxies are concentrated on one side of the Andromeda galaxy. A recent dataset of homogeneous distance measurements for 37 Andromeda satellites highlights this unexpected arrangement.

Specifically, all but one of Andromeda's satellites lie within 107 degrees of the line pointing towards the Milky Way, a region covering only 64% of the host galaxy's surroundings. Until now, it was unclear whether this peculiar configuration significantly challenges the current cosmological model or falls within the range of cosmic variance.

"This asymmetry has persisted and even became more pronounced as fainter galaxies have been discovered and their distances refined," explains Mr. Kosuke Jamie Kanehisa, Ph.D. student at the AIP and lead-author of the study. "Our analyses show that such a pattern is extremely rare in current cosmological simulations."

Modern cosmological simulations, which track galaxy evolution over cosmic time, provide a valuable tool to predict and compare galaxy systems under the standard cosmological framework.

"Using two prominent simulations, we searched for Andromeda-like host galaxies and analyzed the spatial distribution of their dwarf satellites using custom metrics to quantify asymmetry. Comparing Andromeda's observed configuration to these simulated analogs revealed that its satellite distribution is extraordinarily rare," says Dr. Marcel S. Pawlowski from AIP.

"We have to look at more than three hundred simulated systems to find just one that is similarly extreme in its asymmetry as observed." This makes Andromeda an extreme outlier, defying cosmological expectations.

Andromeda's asymmetry becomes even more perplexing when combined with its other unusual feature: half of its satellites co-orbit in a thin, planar structure, reminiscent of planets orbiting the sun. The coexistence of such a plane of satellite galaxies and a lopsided satellite distribution is highly unexpected in the standard cosmological model.

This raises questions about whether Andromeda's evolutionary history is uniquely anomalous or if our understanding of galaxy formation at small scales is incomplete.

Although these findings challenge current cosmological theories, they rely heavily on the accuracy of the underlying simulations, which are limited by how well they model stellar physics and galaxy evolution.

The next steps involve determining whether Andromeda's configuration is a unique outlier or if similarly anisotropic galaxy systems exist elsewhere.

Efforts to study distant systems and search for comparable asymmetries are already underway, and next-generation surveys like Euclid will accelerate this search. Additionally, further analysis of Andromeda's merger history will help determine if such extreme asymmetries can naturally arise in a dark matter-dominated universe—and why they remain absent in current simulations.

[religion-is-a-mental-illness]

By: Steve Stewart-Williams

Published: Mar 5, 2025

Do teachers exhibit gender bias when grading students’ work? If so, in which direction does the bias go? Are teachers more likely to favor boys or favor girls?

These are the questions explored in a fascinating 2020 paper by Camille Terrier, published in the Economics of Education Review. Terrier compared children’s marks on gender-blind national exams with non-blind marks given by their teachers. The findings revealed a persistent marking bias in favor of girls. Although the effect wasn’t huge, Terrier found persuasive evidence that the bias contributes to boys falling behind in school.

Below are some excerpts from the paper. You can read the whole thing here for free.

Background

Boys are increasingly falling behind girls at school. This disadvantage has important consequences: boys who fall behind are at risk of dropping out of school, not attending college or university, and/or being unemployed. In OECD countries, 66% of women entered a university program in 2009, versus 52% of men, and this gap is increasing. In Europe, 43% of women aged 30–34 completed tertiary education in 2015, compared to 34% of men in the same age range. Because this gap has increased by 4.4 percentage points in the last ten years, there is a growing interest in identifying its roots.

Method

I use a rich student-level dataset… that follows 4490 pupils from grade 6 until grade 11. To quantify teachers’ gender biases in math and French, I exploit an essential feature of the data: it contains both blind and non-blind scores. An external grader without knowledge of student’s characteristics provides schools with blind scores. These scores are presumably free of teachers’ biases. Teachers provide non-blind scores for in-class exams… This data allows me to study the effect of teachers’ gender biases on pupils’ progress, schools attended, and course choices.

Quantifying Teacher Bias

[D]espite the commonly held belief that girls are discriminated against, teacher biases favor girls… Figs. 1 and 2 display the distributions of blind and non-blind French scores at the beginning of grade 6… [G]irls’ average score is 0.434 points higher than boys when the score is blind and 0.460 when it is non-blind.

[ Figs. 1 & 2. Test scores for each sex are standardized such that 0 represents the average score. ]

[T]he story is different in mathematics. Figs. 3 and 4 show that boys outperform girls when grades are blind, but the opposite is true when teachers assess their own pupils: girls’ average score at the beginning of grade 6 is 0.147 points lower than boys when the score is blind, but it is 0.170 points higher when the score is non-blind.

[ Figs 3 & 4. Test scores for each sex are standardized such that 0 represents the average score. ]

Knock-On Effects of Teacher Bias

This favoritism, estimated as individual teacher effects, has long-term consequences: as measured by their national evaluations three years later, male students make less progress than their female counterparts… For two classes where the achievement gap between boys and girls would be identical in 6th grade, quasi-randomly assigning a teacher who is 1 SD more biased against boys to one of the classes decreases boys’ progress in that class relative to girls by 0.123 SD in math and by 0.106 SD in French. Over the four years of middle school, teachers’ gender bias against boys accounts for 6% of boys falling behind girls in math… Moving to other outcomes, I find that having a teacher who is one SD more biased in math increases girls’ probability of selecting a scientific track in high school by 3.6 percentage points compared to boys’. Teachers’ average bias in math reduces the gender gap in choosing scientific courses by 12.5%… If teachers’ biases are mainly driven by statistical discrimination, we might expect end-of-year grades to be less biased (and the variance to be smaller) because teachers acquire information about students during the year. On the other hand, if teachers’ biases are mainly taste based, bias should not change over time.1 In that case, end-of-year in-class grades should produce similar bias variance than first-semester grades. The mean and variance of the bias are very similar at the beginning of the year and at the end, suggesting that gender favoritism is mainly taste based.

--

Abstract

I use a combination of blind and non-blind test scores to show that middle school teachers favor girls in their evaluations. This favoritism, estimated as individual teacher effects, has long-term consequences: as measured by their national evaluations three years later, male students make less progress than their female counterparts. On the other hand, girls who benefit from gender bias in math are more likely to select a science track in high school. Without teachers’ bias in favor of girls, the gender gap in choosing a science track would be 12.5% larger in favor of boys.

==

That is, biased marking puts individuals on a science track who would otherwise not qualify, while removing individuals who otherwise would qualify. This is the same situation as Affirmative Action, which artificially altered the natural/unbiased class composition, and which was struck down as unconstitutional.

Some years ago, the very accurate point was made that mean intelligence between males and female is the same, so there's no reason to think girls are any less capable than boys.

Now that the education gender gap has inverted, a common excuse for doing nothing is that, "girls are just smarter than boys." That is, we've pivoted from "all disparities are discrimination" to "these disparities are not just normal but good, ackshully," and we're being gaslighted to pretend we forgot that mean intelligence is the same, even though we've known for years that sex discrimination by teachers is a real thing.

[transgenderer]

I think it's probably a good idea to not give social security to people who are already more wealthy than the average American BUT I do think that there's a weirdness with planning for retirement where like. You don't know how long you're gonna live, there's a large amount of variance in this. So like, you can end up in a situation where you saved a reasonable amount for living 20 years after retirement and then you live 30 years after retirement and you're sort of fucked. So like. We can't totally reduce it to "taxes should only be progressive". Like. Old people are asset-poor in a difficult to quantify way because they can't work (I mean, I guess they can. But not very effectively, for most of them). How much is the capacity to work "worth" to quantify whether a tax is regressive?

[kalux-sims]

Totally positive, non-snarky thoughts on the best features of each game in the franchise, because I'm bored and thinking...

The Sims - wacky humor, creative gameplay

The Sims 2 - animation details, sim personalities

The Sims 3 - open world, Create-A-Style, a lot to do

The Sims 4 - gender variance, appearance details, endless new content

I probably should have done that as bullet points, but oh well. Too hard to add that on a phone after you've written it.

It's also really hard to quantify what makes TS2 so special. I dropped TS the moment TS2 arrived, and never went back. I play TS3 occasionally. I've never played TS4. I think the draw and connection I have with TS2 is the sims themselves. Not having story progression (without a mod) means I play everyone. I'm connected to the whole town. Every birthday. Every new baby. Every wedding. I'm there. I'm living their lives with them, actively. And their personalities are more than you'd think you'd get from the same points style TS has. Between the points, aspirations, and wants/fears, they feel like individuals. They're characters I can connect to, project upon, and care about. It's kept me coming back to TS2 for almost 20 years.

[valorxdrive]

HC; The Heart: The Force Above All.

What's often a popular question amidst the fanbase is what exactly is the Heart. How is it quantified? Do people genuinely run around with two? I want to make one of my comeback Headcanons/Explanations to be set upon this mythical power that All Life holds within the KH Mythos.

To break this down in the most simplest form. The Heart is a Metaphysical essence of Identity for all living things. It's a conceptual form that exists in from its Higher Dimension from all life. The consciousness, definition, information, each of it takes hold of the original position that the Soul has in most media, it is the Self in the purest form. However, it's greatly more vast than the force that normally holds such contents. For you are your Heart before any semblance of Body and Soul.

Soul within the KH mythos takes the place of Life Energy. That in itself is a power that the Heart naturally exudes, no matter how small it's remnant, long as the key Memories (both from the existence and those that know of them) remain. This normally serves as the buffer for the Non-Existence/Nobody kind as they come to actively regrow a Heart. The Heart's essence is returned is by interaction with the World at large, forging experiences with any bit of creation in the Realm of Light/Darkness/In Between.

There actually do exist to Hearts all people from this series hold. The natural one that helps the organic body function, but also what was explained, this metaphysical essence that normally doesn't get tapped into at all for most. In fact, some people can go across their very lives without any meaningful contact this power, and only find themselves utilizing offshoots of it's potential. Such as Mana, and shadowed variances of Light and Darkness.

What's often utilized in terms of the power structure in this franchise in that second Heart. As it stands, there's truly no limit to what could be accomplished by truly gaining Understanding with it. Understanding in itself doesn't hold to the aspect of Knowledge either, rather its a form of Wisdom that can only be experienced above all else, gradually dwindling down the natural barriers upon such potential.

Each Heart holds as that being's very Kingdom, while all Hearts themselves are Kingdom Hearts. For it is the transcendent power of the Heart itself that allows the totality of everything you'll see a Keyblade do in this series. Those who don't even hold a Heart in the KH mytho's meaning normally can't use a Keyblade at all. (There is mysterious workarounds however, as we'll discover in Yozora's arc.)

Forget the potential of the Collective Unconscious known as Kingdom Hearts, just utilizing the potential of your own can draw upon power that can singlehandedly bring cataclysms to multiple universes. Xehanort, one of the series leading antagonists is a prime example of that.

What serves as the essential lifeblood for all Hearts is the natural phenomenon called Memory for the Heart. Your experiences, the very foundational mold for your constitution, stripping this away (from the Heart, not the mind itself) serves one of the most lethal ways to actively not only tear apart existences within this Mythos. One can also utilize those very memories in their own Heart, actively stealing power from others.

The best example of this would be the whole deal between Sora/Xion/Roxas and how the Days storyline really touched on how their abilities flourished.

[amelias-calamity-quintet]

MoaH Races/Species/Lineages Quick Notes

Another quick notes (yes, this is the quick notes, I know), I'm planning to do full cards for these once y'all have met some more characters within these lineages in MoaH.

Existing Lineages

Humans - Humans are generally considered the original species that the Goddesses created. From there, different clans petitioned the Goddesses for certain blessings that would then do magic evolution and create the other species. Chiefly, this applies to the human-like peoples (Hylians, Sheikah, Gerudo, & Dreeka). Humans don't have a whole lot of special blessings, but they are far more adaptable to changes. Unlike other lineages, humans can very easily change their material affinities.

Hylians - Blessed by proximity to the Sacred Realm at the beginning of time, Hylians are generally very similar to humans. They do have improved natural senses, visible by their elongated pointed ears, but aren't quite full blessing recipients compared to other lineages like the Gerudo. Mechanically for Zelda & Link's D&D character sheets, I used half-elf instead of elf, if that helps quantify the level of blessing that Hylians have in setting. Their ears are long enough to be partially emotive. Not full range like the Keaton or Folmir, but they do move up and down more than normal human range to reflect emotion.

Sheikah - No one knows who gave the Sheikah their blessing, and the clan hasn't been forthcoming on sharing that knowledge. There are two identifiable traits for the Sheikah, their eye color, which ranges in reds, and their pointed ears (though not elongated). Other than that, the Sheikah had a natural talent for a number of illusion based magics, even the ability for shapechanging in some cases.

Gerudo - Din's blessed peoples, the Gerudo get some expansion in MoaH that will be saved for more detail in the book set in Rahaal. Most notable for their height and pointed ears, most people will conflate the Hyrulean Gerudo traits for amber eyes and red hair as a common trait as well. Due to, let's say historical tension, with the Gerudo, most Hyruleans aren't familiar with the exceptions.

Gorons - The Gorons in MoaH are largely unchanged from expectation. Generally masc, stone giants, though the composition of that stone does vary by region. Hyrulean Gorons for example have the tan-brown clay coloration we're familiar with, but Gorons from Naydrana have ranges of blue-gray stone reflective of their mountains and Gorons in Teromac have a brighter red common trait, to include some striation like banded stone.

Rito - Rito vary widely on their avian traits based on their heritage. Hyrulean Rito are most often within the predator birds like hawks and owls, though there is some variety. More coastal lineage Rito may take on traits from tropical birds like parrots and seagulls where moving into more mountainous regions see more heavy plumage like grouse and turkeys. The leader of Rito Cleft, Taro, has the plumage of a rock ptarmigan as an example. Their commonalities are chiefly in that they are bipedal with taloned legs and that their wings have a small degree of articulation at the ends to use as hands when not in flight.

Zora - In the time of MoaH, the Sea and River Zora distinctions have blurred a little, chiefly in that they are not limited to Sea and River Zora. Like the Rito, Zora also have a much wider range of traits based on many types of fish. Marela for example draws her traits from jellyfish and beta fish, where the steward of Lake Town and his son, Kije and Kaju, have traits resembling arowana. Commonality here falls in the presence of a tail fin on the head and webbing on the hands and feet. I've mentioned it before, but the height range also is not influenced by gender, there are short and tall Zora of all genders.

Koroks - Koroks in MoaH do take on their more familiar Wind Waker and Wild duo appearances, but they also have some variance based on the forests that they're home to. Most people will be familiar with Hyrulean Koroks and the traits they draw from the Deku Tree. But in Farona, where the most prominent forest entity is the Maru Tree, Koroks have a darker wooden bark and often wear masks in gold and red. There are also Koroks in both places who rather than wearing leaf masks will instead adorn themselves with fungi, like Rephi.

Keaton - Keaton have two forms: a bipedal anthropomorphic fox form and then their full fox forms. The change does alter their size somewhat. Keaton have natural shapechanging abilities and are adapts at illusion magics. Most Keaton will only have one tail, but powerful Keaton mages will develop additional tails. They do not become more human-like or taller. They also will vary in the type of fox they resemble. Hyrulean Keaton most often resemble red and silver foxes, where in somewhere like Farona or Naydrana you'll see more arctic fox traits, and in somewhere like Rahaal you'd likely see traits similar to a Fennec fox.

New Lineages

Dreeka - The Dreeka are a group that departed from the Sheikah way back at the beginning of time, and are now being partially remerged to the Sheikah due to distrust towards Dreeka practices. The Dreeka worship the Horned Goddess as though She is on equal standing to the main Three. As part of this worship, they maintain a series of libraries around the world, with a thorough documentation that spans timelines. It's no small part thanks to the Dreeka that Hyrule has been able to thrive post the Convergence instead of being swallowed by the influx of magic and how that would have shaped society. For folks who read GoS, you might recognize them as the Drex. They are largely the same biologically as the Sheikah, though they have purple irises as a shared trait like the Sheikah have crimson ones, and there's some rumors that they know how to teleport short distances.

Folmir - The Folmir are a shorter species, like Keatons, though their traits vary broadly across the Mustelidae family (ferrets, otters, badgers, polecats, martens, etc). Like the Keaton, they are bipedal, though they do not have transformative magics. They do have semi-prehensile tails that they can train to have more range. They appeared after the Convergence when the influx of magic "awakened" a number of creatures for which they share their primary traits. Because of how the magic expanded, there are very few Folmir living in Hyrule, they've largely settled in other regions like Farona, Lyberic, and Teromac.

Jynird - The Jynird were originally mistaken as a new lineage under the human umbrella, though their distinction quickly became apparent. Like the Folmir, the Jynird were created in the wake of the Convergence, though without anything to bind to, magic made life out of the elements. These became the Jynird, elemental beings as abstract and diverse as magic itself. Very often Jynird will have names representing the concept of the magic they're made of, like Starlight From the First Summer's Eve or Breath Between the Rivers. As I'm not a linguist and am only delving in a very limited scope into linguist research for MoaH, there is not an elemental language that the Jynird can more easily communicate these concepts. Also like the Folmir, the Jynird largely do not live in Hyrule, more populous in other countries like Rahaal, Naydrana, and Holan.

Definitely, definitely no secret fourth option.

[eccentric-nucleus]

i starting thinking about that medieval food labor & land use calculator again and i looked at the code and i still have no clue how to do any of this. there's so much variance in the reported numbers i really have no clue where to start. grain yields of bushels/acre varied from like, 5 to 20. how many acres of land were expected to feed a family varied from 30 to 120. or even further! how many bushels of grain does it take to feed a person for a year? uhhh who can say. it's like a never-ending word problem but also everything has enormous error bars

this isn't even getting into the labor costs per season, which are less well-quantifiable. how many hours of labor does it take to maintain a wheat field? well, it depends on the season. an acre was originally defined as the land two oxen could plow in a day's work, but "what kind of plow" is kind of an important question. and if you change the size of an acre that has implications for all the yield stuff above. aah.

[juliebowie]

Understanding Different Types of Variables in Statistical Analysis

Summary: This blog delves into the types of variables in statistical analysis, including quantitative (continuous and discrete) and qualitative (nominal and ordinal). Understanding these variables is critical for practical data interpretation and statistical analysis.

Introduction

Statistical analysis is crucial in research and data interpretation, providing insights that guide decision-making and uncover trends. By analysing data systematically, researchers can draw meaningful conclusions and validate hypotheses. 

Understanding the types of variables in statistical analysis is essential for accurate data interpretation. Variables representing different data aspects play a crucial role in shaping statistical results. 

This blog aims to explore the various types of variables in statistical analysis, explaining their definitions and applications to enhance your grasp of how they influence data analysis and research outcomes.

What is Statistical Analysis?

Statistical analysis involves applying mathematical techniques to understand, interpret, and summarise data. It transforms raw data into meaningful insights by identifying patterns, trends, and relationships. The primary purpose is to make informed decisions based on data, whether for academic research, business strategy, or policy-making.

How Statistical Analysis Helps in Drawing Conclusions

Statistical analysis aids in concluding by providing a structured approach to data examination. It involves summarising data through measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation). By using these summaries, analysts can detect trends and anomalies. 

More advanced techniques, such as hypothesis testing and regression analysis, help make predictions and determine the relationships between variables. These insights allow decision-makers to base their actions on empirical evidence rather than intuition.

Types of Statistical Analyses

Analysts can effectively interpret data, support their findings with evidence, and make well-informed decisions by employing both descriptive and inferential statistics.

Descriptive Statistics: This type focuses on summarising and describing the features of a dataset. Techniques include calculating averages and percentages and crating visual representations like charts and graphs. Descriptive statistics provide a snapshot of the data, making it easier to understand and communicate.

Inferential Statistics: Inferential analysis goes beyond summarisation to make predictions or generalisations about a population based on a sample. It includes hypothesis testing, confidence intervals, and regression analysis. This type of analysis helps conclude a broader context from the data collected from a smaller subset.

What are Variables in Statistical Analysis?

In statistical analysis, a variable represents a characteristic or attribute that can take on different values. Variables are the foundation for collecting and analysing data, allowing researchers to quantify and examine various study aspects. They are essential components in research, as they help identify patterns, relationships, and trends within the data.

How Variables Represent Data

Variables act as placeholders for data points and can be used to measure different aspects of a study. For instance, variables might include test scores, study hours, and socioeconomic status in a survey of student performance. 

Researchers can systematically analyse how different factors influence outcomes by assigning numerical or categorical values to these variables. This process involves collecting data, organising it, and then applying statistical techniques to draw meaningful conclusions.

Importance of Understanding Variables

Understanding variables is crucial for accurate data analysis and interpretation. Continuous, discrete, nominal, and ordinal variables affect how data is analysed and interpreted. For example, continuous variables like height or weight can be measured precisely. In contrast, nominal variables like gender or ethnicity categorise data without implying order. 

Researchers can apply appropriate statistical methods and avoid misleading results by correctly identifying and using variables. Accurate analysis hinges on a clear grasp of variable types and their roles in the research process, interpreting data more reliable and actionable.

Types of Variables in Statistical Analysis

Understanding the different types of variables in statistical analysis is crucial for practical data interpretation and decision-making. Variables are characteristics or attributes that researchers measure and analyse to uncover patterns, relationships, and insights. These variables can be broadly categorised into quantitative and qualitative types, each with distinct characteristics and significance.

Quantitative Variables

Quantitative variables represent measurable quantities and can be expressed numerically. They allow researchers to perform mathematical operations and statistical analyses to derive insights.

Continuous Variables

Continuous variables can take on infinite values within a given range. These variables can be measured precisely, and their values are not limited to specific discrete points.

Examples of continuous variables include height, weight, temperature, and time. For instance, a person's height can be measured with varying degrees of precision, from centimetres to millimetres, and it can fall anywhere within a specific range.

Continuous variables are crucial for analyses that require detailed and precise measurement. They enable researchers to conduct a wide range of statistical tests, such as calculating averages and standard deviations and performing regression analyses. The granularity of continuous variables allows for nuanced insights and more accurate predictions.

Discrete Variables

Discrete variables can only take on separate values. Unlike continuous variables, discrete variables cannot be subdivided into finer increments and are often counted rather than measured.

Examples of discrete variables include the number of students in a class, the number of cars in a parking lot, and the number of errors in a software application. For instance, you can count 15 students in a class, but you cannot have 15.5 students.

Discrete variables are essential when counting or categorising is required. They are often used in frequency distributions and categorical data analysis. Statistical methods for discrete variables include chi-square tests and Poisson regression, which are valuable for analysing count-based data and understanding categorical outcomes.

Qualitative Variables

Qualitative or categorical variables describe characteristics or attributes that cannot be measured numerically but can be classified into categories.

Nominal Variables

Nominal variables categorise data without inherent order or ranking. These variables represent different categories or groups that are mutually exclusive and do not have a natural sequence.

Examples of nominal variables include gender, ethnicity, and blood type. For instance, gender can be classified as male, female, and non-binary. However, there is no inherent ranking between these categories.

Nominal variables classify data into distinct groups and are crucial for categorical data analysis. Statistical techniques like frequency tables, bar charts, and chi-square tests are commonly employed to analyse nominal variables. Understanding nominal variables helps researchers identify patterns and trends across different categories.

Ordinal Variables

Ordinal variables represent categories with a meaningful order or ranking, but the differences between the categories are not necessarily uniform or quantifiable. These variables provide information about the relative position of categories.

Examples of ordinal variables include education level (e.g., high school, bachelor's degree, master's degree) and customer satisfaction ratings (e.g., poor, fair, good, excellent). The categories have a specific order in these cases, but the exact distance between the ranks is not defined.

Ordinal variables are essential for analysing data where the order of categories matters, but the precise differences between categories are unknown. Researchers use ordinal scales to measure attitudes, preferences, and rankings. Statistical techniques such as median, percentiles, and ordinal logistic regression are employed to analyse ordinal data and understand the relative positioning of categories.

Comparison Between Quantitative and Qualitative Variables

Quantitative and qualitative variables serve different purposes and are analysed using distinct methods. Understanding their differences is essential for choosing the appropriate statistical techniques and drawing accurate conclusions.

Measurement: Quantitative variables are measured numerically and can be subjected to arithmetic operations, whereas qualitative variables are classified without numerical measurement.

Analysis Techniques: Quantitative variables are analysed using statistical methods like mean, standard deviation, and regression analysis, while qualitative variables are analysed using frequency distributions, chi-square tests, and non-parametric techniques.

Data Representation: Continuous and discrete variables are often represented using histograms, scatter plots, and box plots. Nominal and ordinal variables are defined using bar charts, pie charts, and frequency tables.

Frequently Asked Questions

What are the main types of variables in statistical analysis?

The main variables in statistical analysis are quantitative (continuous and discrete) and qualitative (nominal and ordinal). Quantitative variables involve measurable data, while qualitative variables categorise data without numerical measurement.

How do continuous and discrete variables differ? 

Continuous variables can take infinite values within a range and are measured precisely, such as height or temperature. Discrete variables, like the number of students, can only take specific, countable values and are not subdivisible.

What are nominal and ordinal variables in statistical analysis? 

Nominal variables categorise data into distinct groups without any inherent order, like gender or blood type. Ordinal variables involve categories with a meaningful order but unequal intervals, such as education levels or satisfaction ratings.

Conclusion

Understanding the types of variables in statistical analysis is crucial for accurate data interpretation. By distinguishing between quantitative variables (continuous and discrete) and qualitative variables (nominal and ordinal), researchers can select appropriate statistical methods and draw valid conclusions. This clarity enhances the quality and reliability of data-driven insights.

[bwhitex]

Rules for Self-Systems

Use Positive Metaphors

Choose metaphors that empower and motivate you. For example, if you're working on a challenging project, frame it as "climbing a mountain" rather than "fighting an uphill battle". Internal postive valuation is necessary prerequisite for frame control. Actively listening to your own associations, and relational factors, to life, and oppositional forces to that. The relational factors to those too. Positive evaluation would be speaking more affirmatively of yourself and affirmation words towards yourself moderately, or in general. Choose empowering metaphors that frame challenges in a motivating way. This helps cultivate a positive internal narrative.

Reframe Negative Thoughts

Use language to reshape negative thoughts into positive ones. Instead of saying "I can't do this", say "I'll find a way to overcome this". Adopt new vocabulary, like descriptive words that have neutral feelings, and helping you logically explain your own uncertainty. These are sometimes scientific in nature, and require context, utilizing reflexivity. Quantifying by grouping the negative thoughts, matching them up and locating mirroring in others. This allows new insights to be generated, different metaphors that explain sentients, is a break form singularity, thus less traumatic objectively. Generating internal dialogue that makes self system feel authentically positive, is going to going to have potential in making social interactions dependent. Dependent on the internal emotional process of mutual positive valuation. That’s scarce. Transform negative self-talk into positive, solution-oriented language. Adopt neutral, descriptive words that help to objectively assess situations.

Embrace Physicality

Engage in physical activities that reinforce your goals. This could include exercise, meditation, or even something as simple as maintaining a good posture. A social excersize, is one where social experimentation happens. Feel one way positive, and build out from there, consistently, inconsistent of course. But the internal desire for affirmation, feeling grateful, is one that keeps death at bay, truly. Embracing- innocent and positive experiences, it’s partly a history, as that is a baseline, and attitude around the self system is directly correlated, to the interests rates of their attention and time, valation itself as process, can be directive. Participate in physical activities that align with and reinforce personal goals. Recognize the connection between physical states and mental well-being.

Practice Mindful Speech

Be aware of the words you use and how they might be influencing your thoughts and actions. It’s an overall pattern, most importantly, practice psychological flexibility the most. Mental agility is found within time and awareness of language is negative or disempowering, consciously choose to use more positive and empowering language. Be conscious of your own language use and its impact on your thoughts and behaviors. Strive for psychological flexibility and mental agility.

Rules for Social Systems

Understand Others' Metaphors

Pay attention to the metaphors others use and try to understand their perspectives. This can help you to communicate more effectively and empathetically. Metaphors can create a sense of unified language, among real human variances of experience, and by building a common understanding of everyone’s abstract bridges between the speaker’s emotions and listener’s understanding. Theres likelihood of a mor equal chance, that speaker’s and listener’s understandings of abstract structure may guide towards a natural outcome of problem solving better, and feel supported. Pay attention to the metaphors people use to gain insight into their perspective and create more empathetic communication.

Use Language that Resonates

When communicating with others, speakers use language and metaphors that resonate with their experiences. This can help to build rapport and influence. When speaking, focus on truly understanding what the other is saying, means, longer duration of precise sentient profiles. Listen to them for a longer duration of the beginning. Use this period of reflection on the speaker’s keywords, industry jargon, colloquialisms, and even the person’s speech, tempo, and rhythm. Note any unique or characteristic words and phrases, note the frequency of a word too. If at all possible, mentally record these terms for later use. Then implement those same terms or phrases. If they say, “This feels amazing”, you mirror with “that’s amazing, that you felt amazing, tell me more about what felt exciting?” Matching it’s a strategy, not the sole one but is very effective, although, also common. If the speaker speaks plainly, occasionally, speak plainly. Higher the frequency though, not completely. Miror, elevate your vocabulary, if the speaker is elevating theirs too. Increase the frequency more importantly. Excessiveness will create anxiety, from the suspicion of being patronizing. However, doing too less of mirroring and matching and appear one will appear to be potentially to feel like mocking. Match their tone, pace, and volume of their speech. If necessary, moderate your tone consistently to inflict introspection on the crowd of listerners, and do it moderately, frequency wise. Listeners will feel your language validate their ideas and feelings, showing you understand their perspective. Engaging, and adjusting to flow, so asking them the term and phrase the listener is unfamular with then ask the speaker to explain it. Employ language and metaphors that connect with others' experiences to build rapport and influence. Listen attentively and mirror language appropriately.

Challenge Dominant Metaphors

If you notice that a group or organization is using metaphors that are harmful or unhelpful, don't be afraid to challenge them and propose alternative metaphors. Utilizing moderation of speaker’s own speed, tone, tempo, and pace with matching and mirroring strategies. A self-system is able to paraphrase their metaphors, in their own words, reflecting a feeling of attunement between speaker and listener(s). Directive communication and finding a balance between using their own descriptive words when paraphrasing shared metaphors, and the listeners. This balance between how much the listeners know of the speakers’ metaphors, when using them, and speaker’s own, has polarities of style. Some communications in paraphrasing have a complimentary style in balance, it’s uneven but heart felt. Some are more symmetrical, and all others are spectrums of all. Mapping the preference, with initiation is possible. Inquiring, a key aspect of warmth, open ended questions about descriptive vocabulary is crucial to navigating the terrain of listeners. Eliciting feedback on thought and sentiment alignments matters too. Adapting and adjusting will enable flow properly. When encountering unhelpful or negative metaphors within groups, propose more positive or constructive alternatives.

Promote Positive Social Frames

Use language to promote positive frames in social interactions. For instance, frame discussions as opportunities for "collaboration" and "learning" rather than "debate" or "conflict". The first power move is to control the sentients and its’ perceptual understanding too, which isn’t fully possible, every time. This active process of feeding the environments positive sentiments, with the speaker’s tireless effort at positive valuation of others, and reframing. Reframing all negatives, neutral, neutral’s to positive, when it’s possible. The active process here is making the environment dependent on positivity, and adjusted to having it. This changed the collective tone over time, if duration is moderately frequent, reframing will be naturally induces. Change songs, music tones, and even your tone towards moderate. Be direct and honest. Reframe them logically. Encourage a positive outlook in social settings by framing interactions as collaborative and educational instead of adversarial. Both sets of rules emphasize the importance of language as a tool for shaping personal and group dynamics. By fostering positive self-talk and understanding the linguistic cues of others, individuals can better navigate their internal landscape and social environments.

In summary, these rules encourage mindful and strategic use of language and embodied experiences to manage self and social systems. By choosing empowering metaphors, reframing negative thoughts, embracing physicality, understanding others' metaphors, using resonant language, challenging dominant metaphors, and promoting positive social frames, you can use language as a tool to control self and social systems to your benefit.

Lakoff, G., & Johnson, M. (1980). _Metaphors we live by_. University of Chicago Press.

[covid-safer-hotties]

Reference archived on our website

Covid is an intersectional issue.

Abstract

Substantial racial/ethnic and gender disparities in COVID-19 mortality have been previously documented. However, few studies have investigated the impact of individual socioeconomic position (SEP) on these disparities.

Objectives: To determine the joint effects of SEP, race/ethnicity, and gender on the burden of COVID-19 mortality. A secondary objective was to determine whether differences in opportunities for remote work were correlated with COVID-19 death rates for sociodemographic groups.

Design: Annual mortality study which used a special government tabulation of 2020 COVID-19-related deaths stratified by decedents’ SEP (measured by educational attainment), gender, and race/ethnicity. Setting: United States in 2020. Participants: COVID-19 decedents aged 25 to 64 years old (n = 69,001). Exposures: Socioeconomic position (low, intermediate, and high), race/ethnicity (Hispanic, Black, Asian, Indigenous, multiracial, and non-Hispanic white), and gender (women and men). Detailed census data on occupations held by adults in 2020 in each of the 36 sociodemographic groups studied were used to quantify the possibility of remote work for each group.

Main Outcomes and Measures: Age-adjusted COVID-19 death rates for 36 sociodemographic groups. Disparities were quantified by relative risks and 95% confidence intervals. High-SEP adults were the (low-risk) referent group for all relative risk calculations. Results: A higher proportion of Hispanics, Blacks, and Indigenous people were in a low SEP in 2020, compared with whites. COVID-19 mortality was five times higher for low vs. high-SEP adults (72.2 vs. 14.6 deaths per 100,000, RR = 4.94, 95% CI 4.82–5.05). The joint detriments of low SEP, Hispanic ethnicity, and male gender resulted in a COVID-19 death rate which was over 27 times higher (178.0 vs. 6.5 deaths/100,000, RR = 27.4, 95% CI 25.9–28.9) for low-SEP Hispanic men vs. high-SEP white women. In regression modeling, percent of the labor force in never remote jobs explained 72% of the variance in COVID-19 death rates.

Conclusions and Relevance: SARS-CoV-2 infection control efforts should prioritize low-SEP adults (i.e., the working class), particularly the majority with “never remote” jobs characterized by inflexible and unsafe working conditions (i.e., blue collar, service, and retail sales workers).

[strangesmallbard]

Correct Answer: No Role At All

Jesus does not play a role in Judaism. That includes both the Tanach, which is arguably our primary religious text, and the daily religious/cultural practice of Judaism. He certainly plays a role in Jewish history—largely a negative one, as his followers have persecuted Jews for multiple centuries. There is evidence that he may appear briefly in the Talmud, which is a compilation of Jewish legal commentaries and teachings that modern Jewish scholars study throughout their lives. However, this is not a commonly-known fact amongst Jews (I myself didn't know this beforehand), nor do regular practicioners ever discuss him in Torah Study or services. (If any Talmudic scholars want to comment on this, absolutely feel free to!)

Nothing described above constitutes a role in Judaism. One trend I saw in tags was the assumption that, since Jews often engage in scholarly and/or religious debate, we likely discuss Jesus amongst ourselves. We don't lol; if there's a consensus about anything in Judaism, it's probably the Jesus thing. There is slight variance amongst individual Jews; for example, some consider him a wise rabbi/teacher with good ideas, while for others he's just some Jewish guy who probably existed and caused problems.

For every non-Jew who believed he had a role, I urge you to reflect on this assumption. Where did it come from? Who gave you this information initially—a Jew or a gentile? Why might a Christian source want you to believe we accept Jesus as a prophet or prominent figure? I also urge anyone who grew up Christian (or in a Christian culture) to reflect on any emotional reaction you had to learning this information. Were you shocked or uncomfortable? What do you feel about Jews who don't like Jesus at all, no matter how "good" his ideas might be?

While Jesus doesn't play a role in Judaism, Christianity does play a large role in antisemitism. This may also be new information to you. If you feel shame or guilt about your reaction and/or not knowing, I gently urge you to unpack that before engaging in any more conversations—if either emotion is the driving force of your allyship, you will burn out. And here's me talking specifically, because I can't speak for all Jews lmao: you don't need to confess or repent. You just need to show up and trust the perspectives of Jewish people about both our religion/culture and experiences with antisemitism. (You'll notice that all the sources I've included above and below are from Jewish organizations!) That's the first step, and I appreciate any non-Jews who take that step and keep going afterwards.

Results/Quick Analysis:

Thank you to everyone who participated! I was actually blown away by how seriously folks took this question; I've been joking to friends that I haven't ever seen this many goyim be normal towards Jews LMAO + genuinely curious to learn more about our religion, culture, and history. The bar is wildly low, but it's still cool to see it surpassed, and to see an absolute Torah Study happening in those tags.

Very Quick Analysis: the results were both relieving and worrying. Relieving because most people got the right answer! Worrying because, well, nearly half of the respondents did not. This isn't a verifiably solid sample size by any means, but that's still roughly 44% of 44,027 respondents, not including everyone who voted "something else." (Some of those answers veered from "very wrong" to "techically right," so it'd take a bit to accurately quantify).

That being said, the three primary wrong answers are not equally wrong. There are some important and interesting nuances to oberve here, and I plan to do so in a much longer post (hopefully) later this week. However, if you're interested in a quick explanation/breakdown of the wrong answers, click the read more below.

Wrong Answer One: Jesus is a Jewish Prophet

Jesus is not considered a Jewish prophet by any major sects or traditions. There are approximately 48 recorded prophets in the Tanach, none of whom are the guy known to Christians as Jesus. (I say approximately because the number has been contested before in the Talmud). There may be individual Jews who believe that Jesus should be a prophet or a significant religious teacher in Judaism, such as this guy I found in a 1971 New York Times article.

Here's the definition of a Jewish prophet, according to JewFaqs:

A prophet is basically a spokesman for G‑d, a person chosen by G‑d to speak to people on G‑d's behalf and convey a message or teaching. Prophets were role models of holiness, scholarship and closeness to G‑d. They set the standards for the entire community.

In very simple terms, Jesus is not a prophet because we don't believe he spoke on G-d's behalf. I was very tickled by the description of Jesus as a "good Jew" in some tags, because, well. No he ain't, according to most commonly accepted definitions of a Jew who practices Judaism and participates in Jewish culture. (Some disagree with this, however! That is where debates can happen between Jews. Just not in Torah Study).

There are also Messianic Jews/"Jews for Jesus", who have alternative beliefs about Jesus' role in Judaism, to put it mildly. However, Messianic Jews do not reflect the beliefs of anyone but themselves. Many Jews (myself included) do not count Messianic Jews as Jews. To learn why, please read that article I've linked there.

According to the tags, there appears to be two main reasons for this assumption. The first is the one I expected: While Jesus is not a prophet in Judaism, he is a prophet in Islam. This conflation is somewhat understandable, especially for Muslims/those who grew up Muslim, plus anyone who only knows a few facts about either Judaism or Islam.

The second one is honestly shocking to me: some Christian schools (including day schools and extracurricular programs) are apparently teaching y'all that Jews believe Jesus is a prophet!!! Hello lmao. They are Blatantly lying to you! This is fascinating. And it explains so much about Christian assumptions of Judaism and our relationship to JC. But what the fuck. Anyway, I plan to analyze Why I think they're teaching y'all that in the future Big Post. In the meantime: feel free to toss that lesson out. garbage

Wrong Answer Two: Jesus Appears in the Torah

Nope! The Torah's historical timeline is complicated, especially when you consider both the oral traditions and the written text. However, Jesus definitely doesn't appear in there. In general, the Torah describes the first five books in the Tanach, which consists of three major sections: the Chumash (the Torah), the Prophets (Neviim), and the Writings (Ketuvim). The Tanach roughly correlates to the Christian Old Testament; there are some key differences in which texts are included in the latter versus the former.

Anyway, as many have pointed out in the tags, the Torah was written way before Jesus was born. There's no full consensus on when the written Torah (as Jews know it today) was completed, but it was definitely before the birth of JC. He missed the whole party and we're not giving him any party favors.

According to the tags, I believe there are also two main reasons for this assumption. The first is plain and simple ignorance. Many gentiles don't know what the Torah is; in fact, many assume that it's the full Old Testament. Others don't know that Jesus only appears in the new one, especially if they weren't raised Christian/only know stuff about Christianity through osmosis. And it's okay to not know things! But now you know. Woe! Google Scholar be upon ye

The second is a bit more complicated: according to Christian theology, Jesus' birth was predicted in the Old Testament, aka the Tanach, aka the Torah. In this context, it makes sense why Christians/anyone raised vaguely Christian might misremember that Jesus himself shows up. Or they might count these predictions as him "showing up." But this is only true of Christianity. Jews do not believe that Jesus shows up in the Torah. Theologically speaking, that would be as absurd as the Buddha showing up in the Torah.

"Wrong" Answer Three: Jesus is a Rejected Messiah/Religous Figure

Actually, this answer isn't technically wrong. Anyone who voted this answer gets the metaphorical consolation prize. Put simply, Jews very much do reject Jesus as the prophecied Messiah in Judaism. (Someone in the poll reblogs wrote a great explanation as well - I'll either link it here or reblog it after posting this!) Furthermore, some Jews classify him as a "false Messiah" - belonging to a wider group of other Jews who claimed to be a Messiah and were rejected for various reasons.

The reason why I included this answer is because I was interested in how gentiles would intepret it. Some definitely questioned whether this answer should be separated from "no role at all," and others wanted to know my intended meaning first. For the sake of simplicity, my interpretation is this: Jesus can only be a "Rejected Messiah" figure in Judaism if that rejection is active—something we do as a part of everyday Judaism.

However, our rejection of Jesus is ultimately very, very passive. To actively reject Jesus, we'd have to seriously consider him as a contender. His divinity and/or importance would need to be a subject of debate. And he isn't. For the majority of Jews, rejecting Jesus has the same theological relevance as rejecting any major religious figure from a different religion. To use a common phrase from the tags: to us, Jesus is very much just some guy.

In any case, I do think my original hypothesis holds true: selecting this answer over "no role" shows that you approached the question from a Christian perspective, rather than a Jewish one. To be clear, I don't expect you to have that Jewish perspective ready to go. However, the Christians don't only believe we rejected Jesus; many believe that Jews killed him. (Jews did not kill Jesus. If Jesus existed, the Romans killed Jesus). This antisemitic canard is the basis for other antisemitic canards, including blood libel, which has led to multiple pogroms. (Also: many of these pogroms have historically occurred during Passover).

To put it even more bluntly: the Christian belief that Jews reject Jesus gets us killed. That's why it's important to consider the Jewish perspective over the Christian perspective. That's also why it's important to separate the two religions in your mind. Judaism is not incomplete Christianity, nor a proto-Christianity. It's an expansive tradition spanning thousands of years with multiple sects and diverse histories. And Jesus plays no significant role in that tradition.

Concluding Thoughts (Where's the Long Analysis?)

Thanks for reading the short version (haha) of the analysis! I hope to have a longer one out sometime this week, but it may be longer, as I'm hoping to get imput from other Jewish folks before publishing. Everything I described above is within my wheelhouse of knowledge, but the Longer Version requires a research journey and more in-depth sourcing. (That being said, @ Jews, please feel free to offer corrections or alternative perspectives!)

If anyone has any questions about the poll or what I wrote above, feel free to shoot me an ask or dm! (Anon is unfortunately off because I get nervous every time one of my Jewish posts makes the rounds). If I don't know the answer, I'll either provide a source you can check out, consult another Jew who's up for answering questions, or point you in the general direction of where to find the right answer.

[spintaxi]

Body Count

The Great Bedroom Census: Society's Quest for the Perfect "Body Count"

In a world where dating apps have replaced meet-cutes and emojis convey more than words, society has embarked on a noble quest: determining the "ideal" number of sexual partners one should have. A recent study published in Social Psychological and Personality Science suggests that for men, the magic number is 4 to 5 lifetime partners, while for women, it's a modest 2 to 3 . But what does this say about us? Let's delve into the satirical underbelly of these findings. Yahoo 1. The Goldilocks Principle of Sexual Partners Too few, and you're inexperienced; too many, and you're promiscuous. Society seems to favor the "just right" number of partners. This Goldilocks approach to sexual history reflects our obsession with moderation, even in the bedroom. 2. The Double Standard Olympics Men are celebrated for their conquests, while women are shamed for the same behavior. This age-old double standard persists, with women judged more harshly for exceeding the "ideal" number of partners . 3. The Math Doesn't Add Up If everyone is adhering to these "ideal" numbers, who's accounting for the higher averages reported in studies? The discrepancy suggests a gap between reported ideals and actual behavior, possibly due to societal pressures and stigmas. 4. The Secret Keepers Club A significant portion of individuals in relationships choose to keep their "body count" a secret, fearing judgment or conflict . This secrecy underscores the discomfort surrounding open discussions about sexual history. New York Post 5. The Evolutionary Argument Some argue that men are biologically predisposed to seek multiple partners, while women are more selective. However, this perspective often overlooks cultural and societal influences that shape behavior. dailytelegraph 6. The Lie Detector Test Studies have shown that both men and women lie about their number of sexual partners to conform to societal expectations, with men often inflating and women deflating their counts. Glamour 7. The Cultural Variance Different cultures have varying perceptions of promiscuity, with some societies being more accepting of multiple partners than others . This highlights the role of cultural norms in shaping our views on sexual behavior. 8. The Age Factor Younger generations, particularly millennials, are reportedly having fewer sexual partners than previous generations, possibly due to shifting priorities and increased awareness of sexual health. Time 9. The Role of Religion Religious beliefs often influence perceptions of appropriate sexual behavior, with some faiths promoting abstinence or monogamy, thereby affecting individuals' reported and actual number of partners .dailytelegraph+1Psychology Today+1 10. The Impact of Media Media portrayals of sexuality can shape societal norms and expectations, often glamorizing certain behaviors while stigmatizing others, thus influencing individuals' attitudes toward their own sexual histories .

Conclusion

The quest to determine the "ideal" number of sexual partners reveals more about societal anxieties and double standards than about individual behavior. While studies attempt to quantify this elusive number, the reality is that sexual history is deeply personal and influenced by a myriad of factors, including culture, religion, media, and personal values. Perhaps it's time to move beyond the numbers and focus on open, honest conversations about sexuality, free from judgment and societal pressure. Disclaimer: This satirical piece is a collaborative effort between a tenured professor and a philosophy major turned dairy farmer. No AI was harmed in the making of this article.

SpinTaxi Satire - Wide cartoon in the absurd, satirical style of Al Jaffee. Title 'The Ideal Number of Partners Explained by a Panel of Total Strangers.' The scene is ... - SpinTaxi.com 2

15 Observations on the Ideal Number of Sexual Partners

1. Math Class Just Got AwkwardTurns out, algebra was just prepping us to solve for “X”—as in, your number of exes. 2. Goldilocks Was the First PolyamoristToo few partners? You're boring. Too many? You're scandalous. Just right? That’s whoever your mom thinks is decent enough to bring to Thanksgiving. 3. Men Want to Be Experienced; Women Want to Be Virginal ExpertsApparently, men’s ideal number is “enough to look cool,” and women’s is “not enough to be judged by Karen at book club.” 4. Everyone’s Lying AnywayIf you believe the reported numbers, you probably also believe your uncle really “won that Rolex playing bingo.” 5. Relationship History is Now a Credit ReportBefore dating, we now run a full background check. “Oh, you have an 820 Experian score but a body count of 9? Yikes. That’s a hard inquiry.” 6. The Number is 4—Unless It’s 5. Or 3. Or 17 with a strong personalityThe study recommends 4.3 partners. We’re now rounding people to decimals. “He’s half a guy. It was a Vegas situation.” 7. The Virgin-Madonna-Hot-Mess TrifectaA woman with 2 partners is a saint. A woman with 5 is “exploring herself.” A woman with 10? Apparently running for mayor of Tinder. 8. Your Grandma's Number Is Probably Higher Than YoursLet’s not forget: those Boomers invented Woodstock. You think your Bumble streak impresses anyone born during the sexual revolution? 9. Confessing Your ‘Number’ is the New Catholic Guilt“Forgive me, Bumble, for I have swiped. It has been three months since my last committed situationship.” 10. Honesty is NOT the Best PolicyThe only time lying is encouraged in relationships: taxes, height on dating apps, and how many people you've had Netflix and chill with. 11. There’s No “Right” Number—Only What Won’t Get You GhostedYour number just has to be one less than the person you're dating. It’s like a moral Price Is Right. 12. Studies Conducted by People Who Definitely Don’t Get LaidThe scientists who designed this survey were last seen arguing over whether the word “canoodling” is still sexy. 13. Gen Z is Bringing Monogamy Back Like It’s 1956Gen Z says fewer partners = less chaos. Probably because polyamory doesn’t come with a tech support hotline. 14. Your Therapist Knows the Real NumberLet’s be honest: the person who knows your actual count is the one billing you $150/hr and nodding slowly while you cry about Chad. 15. Celibacy is Trending Because It’s Cheaper Than DatingWith rent, Uber, and brunch inflation, “no partners” is the only fiscally responsible lifestyle left. Read the full article