#causal inference

[omegaphilosophia]

The Philosophy of Statistics

The philosophy of statistics explores the foundational, conceptual, and epistemological questions surrounding the practice of statistical reasoning, inference, and data interpretation. It deals with how we gather, analyze, and draw conclusions from data, and it addresses the assumptions and methods that underlie statistical procedures. Philosophers of statistics examine issues related to probability, uncertainty, and how statistical findings relate to knowledge and reality.

Key Concepts:

Probability and Statistics:

Frequentist Approach: In frequentist statistics, probability is interpreted as the long-run frequency of events. It is concerned with making predictions based on repeated trials and often uses hypothesis testing (e.g., p-values) to make inferences about populations from samples.

Bayesian Approach: Bayesian statistics, on the other hand, interprets probability as a measure of belief or degree of certainty in an event, which can be updated as new evidence is obtained. Bayesian inference incorporates prior knowledge or assumptions into the analysis and updates it with data.

Objectivity vs. Subjectivity:

Objective Statistics: Objectivity in statistics is the idea that statistical methods should produce results that are independent of the individual researcher’s beliefs or biases. Frequentist methods are often considered more objective because they rely on observed data without incorporating subjective priors.

Subjective Probability: In contrast, Bayesian statistics incorporates subjective elements through prior probabilities, meaning that different researchers can arrive at different conclusions depending on their prior beliefs. This raises questions about the role of subjectivity in science and how it affects the interpretation of statistical results.

Inference and Induction:

Statistical Inference: Philosophers of statistics examine how statistical methods allow us to draw inferences from data about broader populations or phenomena. The problem of induction, famously posed by David Hume, applies here: How can we justify making generalizations about the future or the unknown based on limited observations?

Hypothesis Testing: Frequentist methods of hypothesis testing (e.g., null hypothesis significance testing) raise philosophical questions about what it means to "reject" or "fail to reject" a hypothesis. Critics argue that p-values are often misunderstood and can lead to flawed inferences about the truth of scientific claims.

Uncertainty and Risk:

Epistemic vs. Aleatory Uncertainty: Epistemic uncertainty refers to uncertainty due to lack of knowledge, while aleatory uncertainty refers to inherent randomness in the system. Philosophers of statistics explore how these different types of uncertainty influence decision-making and inference.

Risk and Decision Theory: Statistical analysis often informs decision-making under uncertainty, particularly in fields like economics, medicine, and public policy. Philosophical questions arise about how to weigh evidence, manage risk, and make decisions when outcomes are uncertain.

Causality vs. Correlation:

Causal Inference: One of the most important issues in the philosophy of statistics is the relationship between correlation and causality. While statistics can show correlations between variables, establishing a causal relationship often requires additional assumptions and methods, such as randomized controlled trials or causal models.

Causal Models and Counterfactuals: Philosophers like Judea Pearl have developed causal inference frameworks that use counterfactual reasoning to better understand causation in statistical data. These methods help to clarify when and how statistical models can imply causal relationships, moving beyond mere correlations.

The Role of Models:

Modeling Assumptions: Statistical models, such as regression models or probability distributions, are based on assumptions about the data-generating process. Philosophers of statistics question the validity and reliability of these assumptions, particularly when they are idealized or simplified versions of real-world processes.

Overfitting and Generalization: Statistical models can sometimes "overfit" data, meaning they capture noise or random fluctuations rather than the underlying trend. Philosophical discussions around overfitting examine the balance between model complexity and generalizability, as well as the limits of statistical models in capturing reality.

Data and Representation:

Data Interpretation: Data is often considered the cornerstone of statistical analysis, but philosophers of statistics explore the nature of data itself. How is data selected, processed, and represented? How do choices about measurement, sampling, and categorization affect the conclusions drawn from data?

Big Data and Ethics: The rise of big data has led to new ethical and philosophical challenges in statistics. Issues such as privacy, consent, bias in algorithms, and the use of data in decision-making are central to contemporary discussions about the limits and responsibilities of statistical analysis.

Statistical Significance:

p-Values and Significance: The interpretation of p-values and statistical significance has long been debated. Many argue that the overreliance on p-values can lead to misunderstandings about the strength of evidence, and the replication crisis in science has highlighted the limitations of using p-values as the sole measure of statistical validity.

Replication Crisis: The replication crisis in psychology and other sciences has raised concerns about the reliability of statistical methods. Philosophers of statistics are interested in how statistical significance and reproducibility relate to the notion of scientific truth and the accumulation of knowledge.

Philosophical Debates:

Frequentism vs. Bayesianism:

Frequentist and Bayesian approaches to statistics represent two fundamentally different views on the nature of probability. Philosophers debate which approach provides a better framework for understanding and interpreting statistical evidence. Frequentists argue for the objectivity of long-run frequencies, while Bayesians emphasize the flexibility and adaptability of probabilistic reasoning based on prior knowledge.

Realism and Anti-Realism in Statistics:

Is there a "true" probability or statistical model underlying real-world phenomena, or are statistical models simply useful tools for organizing our observations? Philosophers debate whether statistical models correspond to objective features of reality (realism) or are constructs that depend on human interpretation and conventions (anti-realism).

Probability and Rationality:

The relationship between probability and rational decision-making is a key issue in both statistics and philosophy. Bayesian decision theory, for instance, uses probabilities to model rational belief updating and decision-making under uncertainty. Philosophers explore how these formal models relate to human reasoning, especially when dealing with complex or ambiguous situations.

Philosophy of Machine Learning:

Machine learning and AI have introduced new statistical methods for pattern recognition and prediction. Philosophers of statistics are increasingly focused on the interpretability, reliability, and fairness of machine learning algorithms, as well as the role of statistical inference in automated decision-making systems.

The philosophy of statistics addresses fundamental questions about probability, uncertainty, inference, and the nature of data. It explores how statistical methods relate to broader epistemological issues, such as the nature of scientific knowledge, objectivity, and causality. Frequentist and Bayesian approaches offer contrasting perspectives on probability and inference, while debates about the role of models, data representation, and statistical significance continue to shape the field. The rise of big data and machine learning has introduced new challenges, prompting philosophical inquiry into the ethical and practical limits of statistical reasoning.

[urupotter]

I wonder how often studies finding that X activity is bad for you (or that doing it excessively is bad for you) are just picking up on the fact that a day has 24 hours, so any time you dedicate to an activity necessarily crowds out other, potentially benefitial, activities. (same argument works the other way around, studies finding good effects may just be picking up crowding out of bad activities).

[jcmarchi]

LLMs Are Not Reasoning—They’re Just Really Good at Planning

New Post has been published on https://thedigitalinsider.com/llms-are-not-reasoning-theyre-just-really-good-at-planning/

LLMs Are Not Reasoning—They’re Just Really Good at Planning

Large language models (LLMs) like OpenAI’s o3, Google’s Gemini 2.0, and DeepSeek’s R1 have shown remarkable progress in tackling complex problems, generating human-like text, and even writing code with precision. These advanced LLMs are often referred as “reasoning models” for their remarkable abilities to analyze and solve complex problems. But do these models actually reason, or are they just exceptionally good at planning? This distinction is subtle yet profound, and it has major implications for how we understand the capabilities and limitations of LLMs.

To understand this distinction, let’s compare two scenarios:

Reasoning: A detective investigating a crime must piece together conflicting evidence, deduce which ones are false, and arrive at a conclusion based on limited evidence. This process involves inference, contradiction resolution, and abstract thinking.

Planning: A chess player calculating the best sequence of moves to checkmate their opponent.

While both processes involve multiple steps, the detective engages in deep reasoning to make inferences, evaluate contradictions, and apply general principles to a specific case. The chess player, on the other hand, is primarily engaging in planning, selecting an optimal sequence of moves to win the game. LLMs, as we will see, function much more like the chess player than the detective.

Understanding the Difference: Reasoning vs. Planning

To realize why LLMs are good at planning rather than reasoning, it is important to first understand the difference between both terms. Reasoning is the process of deriving new conclusions from given premises using logic and inference. It involves identifying and correcting inconsistencies, generating novel insights rather than just providing information, making decisions in ambiguous situations, and engaging in causal understanding and counterfactual thinking like “What if?” scenarios.

Planning, on the other hand, focuses on structuring a sequence of actions to achieve a specific goal. It relies on breaking complex tasks into smaller steps, following known problem-solving strategies, adapting previously learned patterns to similar problems, and executing structured sequences rather than deriving new insights. While both reasoning and planning involve step-by-step processing, reasoning requires deeper abstraction and inference, whereas planning follows established procedures without generating fundamentally new knowledge.

How LLMs Approach “Reasoning”

Modern LLMs, such as OpenAI’s o3 and DeepSeek-R1, are equipped with a technique, known as Chain-of-Thought (CoT) reasoning, to improve their problem-solving abilities. This method encourages models to break problems down into intermediate steps, mimicking the way humans think through a problem logically. To see how it works, consider a simple math problem:

If a store sells apples for $2 each but offers a discount of $1 per apple if you buy more than 5 apples, how much would 7 apples cost?

A typical LLM using CoT prompting might solve it like this:

Determine the regular price: 7 * $2 = $14.

Identify that the discount applies (since 7 > 5).

Compute the discount: 7 * $1 = $7.

Subtract the discount from the total: $14 – $7 = $7.

By explicitly laying out a sequence of steps, the model minimizes the chance of errors that arise from trying to predict an answer in one go. While this step-by-step breakdown makes LLMs look like reasoning, it is essentially a form of structured problem-solving, much like following a step-by-step recipe. On the other hand, a true reasoning process might recognize a general rule: If the discount applies beyond 5 apples, then every apple costs $1. A human can infer such a rule immediately, but an LLM cannot as it simply follows a structured sequence of calculations.

Why Chain-of-thought is Planning, Not Reasoning

While Chain-of-Thought (CoT) has improved LLMs’ performance on logic-oriented tasks like math word problems and coding challenges, it does not involve genuine logical reasoning. This is because, CoT follows procedural knowledge, relying on structured steps rather than generating novel insights. It lacks a true understanding of causality and abstract relationships, meaning the model does not engage in counterfactual thinking or consider hypothetical situations that require intuition beyond seen data. Additionally, CoT cannot fundamentally change its approach beyond the patterns it has been trained on, limiting its ability to reason creatively or adapt in unfamiliar scenarios.

What Would It Take for LLMs to Become True Reasoning Machines?

So, what do LLMs need to truly reason like humans? Here are some key areas where they require improvement and potential approaches to achieve it:

Symbolic Understanding: Humans reason by manipulating abstract symbols and relationships. LLMs, however, lack a genuine symbolic reasoning mechanism. Integrating symbolic AI or hybrid models that combine neural networks with formal logic systems could enhance their ability to engage in true reasoning.

Causal Inference: True reasoning requires understanding cause and effect, not just statistical correlations. A model that reasons must infer underlying principles from data rather than merely predicting the next token. Research into causal AI, which explicitly models cause-and-effect relationships, could help LLMs transition from planning to reasoning.

Self-Reflection and Metacognition: Humans constantly evaluate their own thought processes by asking “Does this conclusion make sense?” LLMs, on the other hand, do not have a mechanism for self-reflection. Building models that can critically evaluate their own outputs would be a step toward true reasoning.

Common Sense and Intuition: Even though LLMs have access to vast amounts of knowledge, they often struggle with basic common-sense reasoning. This happens because they don’t have real-world experiences to shape their intuition, and they can’t easily recognize the absurdities that humans would pick up on right away. They also lack a way to bring real-world dynamics into their decision-making. One way to improve this could be by building a model with a common-sense engine, which might involve integrating real-world sensory input or using knowledge graphs to help the model better understand the world the way humans do.

Counterfactual Thinking: Human reasoning often involves asking, “What if things were different?” LLMs struggle with these kinds of “what if” scenarios because they’re limited by the data they’ve been trained on. For models to think more like humans in these situations, they would need to simulate hypothetical scenarios and understand how changes in variables can impact outcomes. They would also need a way to test different possibilities and come up with new insights, rather than just predicting based on what they’ve already seen. Without these abilities, LLMs can’t truly imagine alternative futures—they can only work with what they’ve learned.

Conclusion

While LLMs may appear to reason, they are actually relying on planning techniques for solving complex problems. Whether solving a math problem or engaging in logical deduction, they are primarily organizing known patterns in a structured manner rather than deeply understanding the principles behind them. This distinction is crucial in AI research because if we mistake sophisticated planning for genuine reasoning, we risk overestimating AI’s true capabilities.

The road to true reasoning AI will require fundamental advancements beyond token prediction and probabilistic planning. It will demand breakthroughs in symbolic logic, causal understanding, and metacognition. Until then, LLMs will remain powerful tools for structured problem-solving, but they will not truly think in the way humans do.

[ingorohlfing]

"Resolving empirical controversies with mechanistic evidence" - Some thoughts

Recently, I came across the open access article Resolving empirical controversies with mechanistic evidence. I have some thoughts on the arguments made in the article, but first a disclaimer: This is not meant to be a disciplinary beauty contest. The article raises a valuable point: Two or more quantitative models may produce contradictory results. Depending on the research question at hand, it…

[eggwhiteswithspinach]

losing it over this lol

[philosophybits]

From causes which appear similar, we expect similar effects. This is the sum of all our experimental conclusions.

David Hume, An Enquiry Concerning Human Understanding

[ban-joey]

anyway today i test how political is too political for publishing a social epidemiology paper in the current climate

[resignedhope]

i am looking for motivation to study... but not in the neoliberal market-oriented sense where i can be a competitive potential hire for a company that will view me as nothing more than a wage to pay... but in the human sense where i earnestly invest my time and energy in something that piques my interest, adds value to my understanding of the world, and just makes me happy to be a part of this life

[procrastinationstationnn]

what’s the assignment??

coding assignment not actually sure what the specifics are since i haven’t opened it yet heh<3

[autonomy1]

corner of night

a later made

nowhere

or by you perhaps

a closing shape

against the small light remaining

by you perhaps through me because

your least

now moments

each causal/possessing/inferring

each human hid emptiness

arriving-

[strangelittlestories]

The wizard sat in the cell. Their hands were bound in iron. Glyphs glowed on the ceiling, their light filled her with fizzing energy. She hadn't slept in some time.

Fine, they could take her books and her staff. They could deny her knowledge and rest. She had done her best work in the college insomniac and resource starved. She had remade herself with sacred alchemy and experimental thoughtcraft while running on nothing but tea and firefly-root. She could work this problem.

She went over her defense in her head one more time.

“Your honour, in my time bearing the staff I have done many things. I have plundered the heavens for their secrets. I have given monarchs prophecies I knew they would try to escape and, in doing so, wreck themselves. They deserved to get wrecked. But your honour-”

[If you want, we can start now.]

“Pardon me?”

The voice had come from all around her. It resonated through the walls, rattled her chains and her bones. It sang in her blood.

[My apologies for the interruption. You can go on if you like? But it seems you have your arguments well rehearsed already.]

“I have always been… thorough.”

[It is good to be thorough. You should give all your endeavours Due Process.]

The voice was all-encompassing, all-surrounding, a ‘words etched in granite’ sort of voice. But it also almost seemed kind. Or, if not kind, then *thoughtful*.

“Oh heck it, let's get this over with.” The wizard looked up, trying to look the voice in the face even though it had none. “Do we not need a jury or something?”

[No. It has been deemed that your words could be, well, corruptive. Sorry to be so blunt. I shall be your sole judgement. And your ‘soul’ judgement, come to that.]

The wizard was used to peering through the veil to see hidden truths. It was something of an effort to *listen* through the veil instead, but the principle was the same. What they heard was an echo of something gentle but unyielding, something soft but with the weight of mountains behind it.

“First, tell me which god you are.”

[You *are* quick on the uptake. They said you would be. I am Arbiter. I manage the discourse between what is and what is not. I oversee the conversation between consensus and individual. I listen to what agreements have been made and I judge when they have been broken.]

“Second, tell me what I am accused of.”

[You stand accused - or sit accused, I suppose - of breaking the laws of reality.]

“Any in particular?”

[Oh, tons. Gravity. Causality. Probability. Conservation of energy. One one weird one about things going wrong. You name it, you probably broke it.]

“And who wrote these laws? What court or nation drew them up?”

[No mortal court did this.]

“A divine one then?”

[No gods, either. Some of us gods made the planet you live on, some of us made you, but reality’s laws are fundamentally an aspect of Truth. And Truth is an altogether different entity. If it can be an entity at all.]

“Fascinating.” The wizard felt her mind run off in a dozen different directions at the implication of this. She wrenched it back on track. “So Truth is putting me on trial?”

[Philosophers are putting you on trial. They call themselves Absolutists. They hold that acts of magic that bend or break reality are damaging to the Inferred Axioms.]

“So … all magic, then?”

[I am afraid so.]

“If it runs counter to axiomatic truths, then why is magic even possible? Surely, if it can reliably act on the world, it is a fundamental force of reality like any other?”

[This is your defense?]

“This is curiosity.” The wizard clinked their chains in frustration. She wished she could draw upon the walls.

[It is not like other forces, however. Its rules change. Its conventions vary across lands and are inconsistent with each other. It is a trick of Perspective, which does not always get along with Truth, for Perspective plays sleight of hand with the universe. It makes things true just by getting you to look at things the right way for long enough.]

“Alright, here’s my defense.” The wizard let out a deep breath and focused on a spot on the wall and imagined that patch of stone to be the face of Arbiter. Thus, looking the god in the face, the wizard continued, “Screw you.”

[This defense is… unconventional.]

“Listen, buddy. Your honour. Your honoured buddy.” The wizard drew up her shoulders and prepared herself to really go off on one. “You seem like a nice god. But, ultimately, all gods are servants. That’s not a bad thing! Acts of service are beautiful. Sadly, the people you’re serving are assholes and, what’s worse, I think you know that. But you’re so wrapped up in the nobility and importance of your purpose that you don’t seem to care what side you actually end up on or who is standing beside you. And that means you’re not really a servant, you’re a *lackey*.

“It’d be easy to shrug that off and say, oh well, can’t really blame Arbiter, can I? Gods are just like that. But I *have* to believe it’s not that simple. I must believe that you can change and you can choose. And maybe that goes against some divine law or axiom, but baby, I guess I’m just prone to *magical thinking*.

“And it galls me. It does, it galls me, that of all the many things I’ve done… what actually gets me convicted may well be something I *am*. Because if magic is just a way of thinking things might be different, then getting reality itself to - even if just for a moment - see it your way? Then, honoured buddy, I am magic down to the last mote of me.

“The laws of reality? What does that even mean? They’re not laws, not really. They’re just things that *are*. I don’t give a single toot about things that just are. I have no time at all for things that are only ever one thing. I care about what *can* be. And you, my friend, *can* screw off.”

[Unfortunate. If you will not make a proper defense, the philosophers will keep you here indefinitely, so as to limit your impact on reality. They would kill you, but they are scared about ghosts.]

“Then I guess I’ll just have to try and outlive them. Heck, maybe I’ll outlive you too.”

[They are an entire people. And I am eternal.]

“So I guess it’s a longshot, huh?” The wizard spat a thick gob of saliva at the part of the wall where she imagined Arbiter’s face. “Well, I guess I’m pretty comfortable with a longshot.”

---

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[babextoken]

let your feelings slip, boy, but never your mask

⟡˙⋆ chapter 2

warning: inferences to sexual harassment of the reader by another male character pairing: vessel x fem!reader summary:  vessel's determination to mind his business is ruined by your presence wc: 2k head's up: series, slowish burn, enemies to lovers, coworkers, plus size reader, rude!vessel, creeper behavior from Vessel, possibly demi!vessel, inferences to sexual harassment, whump, blood, fist fight, hurt/comfort, sexual tension while cleaning wounds 𓈒⟡₊⋆∘˚⊹ Situation Enjoyers™: @lifemod17 @glitterghost @inv3ga @adenobabe  @jeriiicho @milk--bones  @myaudiocommentary  @horsebiologist @intake-of-breath @fruitsandcheese @killed-by-thegods @goosepond69 @friendly-neighborhood-ghoul @lynzeequitlollygagging @thatxxjiyong-ssi  @cloudy-soul @daddysaidbringthethunder  @evisnotok @cheomain @chaosandchaos @object-of-my-desire @dreamer-lost-in-wonderland @blvckmvgicwoman @canopies-of-gold-and-evergreen @thewayyoulay @houseofsleeptoken @jerrysghostwriter @music-lover23 @renegadebirch @blackcherrywhiskey

recommended listening:

Your expression when you apologized to Vessel haunted him. Objectively, you deserved to be called out. But why did you look like you were 10 years old, folding for mummy, or perhaps worse, your current age, trying to avoid a man’s hand? Vessel’s idle thought of “who made her like this” ends very quickly. He does not have time to wonder about a coworker, much less you. 

Vessel prided himself on work-life separation, even going so far as training himself to not think about work the second he hopped on the train home. Sure he might see a coworker out and about, but that was it. Work was work, and Vessel’s time was his own…until you showed up. You and your ability to both charm customers and then become the most petulant little thing the second they left. When you weren’t in, he causally dropped hints that you annoyed him greatly, thought something was a little off about you. But your coworkers wouldn’t engage. “She’s never been like that with me, what are you on about, Ves?” was always the response. So he did what any student of life would do: watching you. 

When the store was quiet and there were one or two other coworkers with you and Vessel, he would separate from the group. “Straighten the popular titles.” Run some reports (which ones? Uh…requests? He didn’t know. He just knew some people ran reports and damnit he could say he was doing it too). Eventually he’d find a spot to watch from…see you act like a completely different person. You were warm with the rest of the team. You asked questions. The first time he noticed this, Vessel felt his fists clenching into tight little balls of fury. And a first time begets a second. And a third. And then as many as it takes to become routine. This job allows Vessel to make his music on the side, it keeps him going. But you and your ability to overshare and yet say absolutely nothing at the same time grates on him. The idea that you think you’re so great that you can deny Vessel’s the “pleasure” of knowing you taints his every waking thought.

Everyone else figured he had some special project to work on but no, he kept roosting somewhere like a bird of prey waiting for old livestock to die. He was so sure, so poised for the next time he saw you act so outrageously against form, so…contrary to the work culture (he was grasping at straws), that he’d grab you and yell, “you liar, you two faced skank, I need this now GET OUT.”

A crash. When you looked over, you saw Vessel repositioning the cardboard standees for the newest superhero franchise, one with a Vessel’s-shoe-shaped-dent at the bottom. 

“Ya ok?” You call out lamely.

“Just great. Taking my 15.” 

Ves cared too much about his voice to start smoking but the built-in breaks were looking mighty appealing right now. As he sat against the brick wall on this particular day he felt completely exhausted. He wasn’t sleeping well. For weeks on end it was the Sisyphean nightmare of sleeplessness ->accomplish a lot of good work on the EP in the middle of the night-> realize it’s not his best effort -> attempt to sleep and start anew -> have yet another brain-breaking wet dream about you. 

For all intents and purposes Vessel didn’t want sexual feelings. Not because he was asexual, not because he was trying to be stoic and straight edge, he just didn’t want them. Sexual feelings, the motivation to act on them, lead to distraction and perhaps heartache. When people pressed he grew angry--“Ves, bring your guitar to the party, man, you’ll be fighting off pussy left right and center!” No. No no no no no. He didn’t want to. What’s so wrong with that? Getting off alone was just fine. Let me just take care of my business, thank you very much. Honestly, that’s part of the reason he took it so weirdly when you hit on him. He doesn’t need that kind of companionship and he’s finally silenced the part of him that says “hey man, you might not REQUIRE sex and love and closeness, but there’s nothing wrong with just wanting it!” All that hard work and then you happen. 

When Vessel comes back from clearing his head, he notices the guy you were flirting with is back. It’s been awhile since that night when he first came in, maybe a month or two. He comes in a lot and always wants to talk to you. But today something was off. Ves knew it before he even saw the guy, he just saw you making prolonged, intentional eye contact with him. When Ves joined you at the checkout counter, you excused yourself from the guy.

“Ves, before I forget, I need you to tell me again about that training.”

Training? What fucking training? Vessel had half a mind to lose his shit, thinking he forgot a training. Instead he’d uphold workplace standards and be COOL (a curious, open-minded, and observant listener that is. He wanted to throw up where he stood). “I…don’t remember what training you’re talking about.” He’s never felt so confused in his life, mainly because you’re being….normal with him? You grit your teeth in a nervous, queasy smile. Oh. As if someone has said “blink twice if you aren’t safe,” you start batting your lashes with intent, your hands shaking where the guy can’t see. “Oh yeah, yeah, right. That training. The training..we…have to do. I’ll check him out,” Ves points to the guy, “and you go in the back and get the uh…uhm…manual or, whatever we need.” 

You don’t need telling twice. You scurry to the back, and the guy looks less than pleased. Ves goes through the motions of asking how he was and if he found everything alright. The guy just sniffs and says something to the effect of “I had before you walked up here.” It’s unlike him, but Vessel watches the guy get in his car and leave, not moving from the glass door of the store until he sees the guy go through the intersection. You come out of the back looking a little calmer, but blank.

“Thanks,” is all you that say. In fact, it’s all you say to him for the rest of the shift unless a customer comes in, but it’s a slow night for some reason, so Vessel doesn’t get ammo to use against you. You say nothing contrarian. Nothing annoying. Just nothing. 

He actually sleeps that night. No dreams. He doesn’t even think he moved, waking up in the same position he settled in after he turned off the light. And for a while, his nights are like this. Quiet. The EP gets closer to being done. He’s proud. Work doesn’t seep into his mind when he’s not there. And when he is there, he gives you a tight smile, which you barely return, and the shift goes on. The world still spins.

One night he’s closing with you, Vessel actually feels talkative. It’s like your first shift all over again. He’s talking. He’s dominating part of the conversation…but you don’t join in. In fact, you look worse for wear. It’s as if you and Vessel were sharing custody of twin demeanors: this time Vessel had the talkative, happy-go-lucky silly billy and you had the fatigued, sullen, isolated one. And you know what? Vessel felt ok with that. ‘Hah. I’m the adjusted one!’ But when the other coworkers leave because they simply do not need 4 people closing, Vessel doesn’t feel keen to keep talking, or even to gloat. The issue of who will take out the trash comes up again. You are insistent. Almost fiendish when you tell him you’re taking the trash out tonight. 

“Fine. Knock yourself out,” he says with some mirth. But when you’re not back at an appropriate time, he worries a little…that is until he sees your favorite customer’s car in the carpark. Oh here it is. The moment we’ve all been waiting for. With an evil smirk, he walks out to the back of the store ready to bust you giving head in the alley while on the job. Vessel would be the judge, jury, and executioner tonight and he could not…

“I said you need to leave me the fuck alone. I don’t want to do this with you here or anywhere anymore.”

His stomach drops.

“Oh, I don’t think I will, pet.” That time it was the customer. The guy. His voice was rough, breathy, like he was….oh fuck no. 

Vessel busts through the cracked back door and feels every ounce of his blood boil. His bones turn to steel—not to stay put but to crush. To destroy. One might say this was a private moment, but it was Vessel’s business. “Get off her.”

The guy huffs and looks over at Vessel, his hot breath fanning out over his arm that cages you against the grimy wall. “Was just telling her you seemed like a pervy big brother type…you wanna watch?”

Vessel is not of this world. One moment he is on the stairs watching this scum bag attempt to lift your shirt, and the next he feels the pervert’s leather jacket clad shoulder in one hand…and then there’s the pain. The sharp intake as teeth cut into the tender, wet flesh of one’s mouth. The growl that emanates from Vessel is…horrifying. Especially when blood drips down his chin and shirt. You hope to never hear it again, along with the huffs and sloshing sounds of the man’s mouth lolling open as Vessel lands his own punch on him. Vessel stops and holds the man close to whisper in his ear. You can’t make out what he said, but the man looks horrified. Piss-your-pants scared. He tells you and Vessel to fuck off, staggering to his car. 

The store should have been long closed when you both get back inside. Vessel sported a split lip that desperately needed attention. He calms himself in the break room when you approach with a wet rag. You get ready to press it to his lip before stopping and chuckling softly. 

“Obviously you can take care of yourself. Sorry—“

“No.” He whispers shakily but stares blankly…almost sternly. “You’ll do it for me.” 

You gulp and obey. The cold, drenched paper towel makes him wince. Tears fill his eyes but he stares into yours like he has something to prove. The bleeding stopped but the wound looked raw and painful. Perhaps it was a good thing the boy never smiled at you because otherwise his lip might never heal. “You should probably put some…I don’t know…Vaseline on there.”

“Did I do good for you?”

“What?”

“Did. I. Do. Good? Simple question.” He sniffles and swallows back his tears. “Gotten myself all fucked up haven’t I?”

“V, what the fuck are you on about?” You suddenly feel entrapped. The captive audience for his comedown from the adrenaline.

“Just…answer the fucking question,” he seethes.

You put your hands up in surrender. “Yes. I wish you hadn’t gotten yourself hurt, but yeah. You did good.”

He takes in a deep breath that seems almost pleasurable to him. His tongue darts over the wound and a small smirk plays at his lips. “Did you like it?”

“Like what?”

“Being defended. Having…someone stand up for you. Hiding behind me while I took the brunt. Go on. Admit it. Wasn’t it nice to have someone do that for you?”

Your stomach drops. You aren’t sure why. Fear? Anxiety? Arousal? Perhaps all of them. The adrenaline still coursed through your body but that energy wasn’t going towards fleeing. Did that mean you should be here? That this was supposed to happen. “Y…yeah. Yeah. I liked…not having to deal with it myself,” you reply meekly. 

“There’s a good girl. Happy someone has battle scars for you?”

“You’re sick.”

“And I intend to show you that you are, too.” You step back instinctively at his bold assertion but you notice that seemed to hurt him. Out of nervous habit, Ves bites his lip but winces. Yet another indication that he’s capable of human reactions. “I know what you are.”

As if in a mirror darkly, you both realized you had indeed met your match.

[iamrunning-low]

I can't believe I hadn't noticed this before but LOOK:

David Marcus has always felt underdone and underappreciated. But even with so little screen time they obviously tried to make every scene count. And his story is incredibly compelling, with added heartbreak because most people in the fandom don't seem to care or feel the full impact of the tradeoff Kirk makes with Spock and David between wrath of khan and search for spock. So to have Kirk cover David with his jacket but not actually show him do it hurts so much, but it feels very poetic at the same time.

Most of David's plot has to be inferred from the clues we get from the few scenes we do get from him and from what other characters say about him. We understand most of the feelings around him and his situation based on what Kirk shows us. In a way, he haunts the narrative before he even dies(if there's an actual term for that, let me know).

Until I actually thought about it and watched it again, this scene felt incredibly cold. But Kirk already had his breakdown, he's already let himself fall to the ground in grief. He can't stop again while they're all still in danger. So this moment feels almost generous. One last rite before the planet takes David's body in its self-destruction.

Bones and Scotty see this play out, but they don't say anything. David remains mostly unspoken. He is an unaccounted causality until Kirk shows his care and regard by leaving his jacket with David.

Even more heartbreaking, David doesn't need the jacket, but it's the only thing Kirk can do for him in their situation. A heavy contrast to Spock's funeral. In a way, it's more for Kirk's comfort than David's.

[philosophybits]

Animals are not guided in [causal] inferences by reasoning: Neither are children: Neither are the generality of mankind, in their ordinary actions and conclusions: Neither are philosophers themselves, who, in all the active parts of life, are, in the main, the same with the vulgar, and are governed by the same maxims.

David Hume, An Enquiry Concerning Human Understanding

[from-izzy]

let me briefly tell you something about the psychology of misinformation:

when we hear of events, we will most likely retain the information that we are first exposed to, given that the information is (at least at the surface level) credible

through those information, we build mental models of the events to make sense of the timeline

if the mental model is incomplete, we tend to fill that space with ANY information whether it's something we heard, something that is inferred or something that we make. why? because we like complete incorrect models than incomplete models

some people retain misinformation EVEN after correction - this is due to a lot of factors that i won't even talk about

one of the best way to correct misinformation is when there is a causal explanation to the event to complete the mental model

so i guess what i'm saying to all of this is: yes, we are cognitive misers and we hate thinking, but at the very least, take your time to be uncomfortable for a bit UNTIL credited, real information is out.

and i guess what i'm trying to say further is, i think we as a whole, humans as a whole, need to sit down, take a deep breath and gain all the facts before we start pointing fingers and accusing people of a, b and c without definite reason/evidence.

and i guess what i'm trying to say is, in this moment with the information given and available, i think the boyz should stay as 11.

[hungwy]

Lol this whole entire story about how you can't tell what things are when you aren't picturing them clearly just so you can say the word "incontrovertibly" to try and impress people online and flaunt your fake scholarly personality around. How ungraceful. How conspicuous. That's like a basic normal thing that happens to everyone. You're just saying "I can't tell what things are if I can't tell what those things are." Like? What a redundant tautology? If you're really that desperate for praise about your vocabulary, why don't you try actually saying these words normally, without extra thought and deliberation for starters, and also be more brief and graceful as far as why you're even saying what you're saying in the first place. Wow it's so painful to be around your theatrical persona that seeks attention and praise in this way that's about not appearing to do so and maintaining plausible deniability because you're at least smart enough to know that straightforward attention seeking will only undermine your efforts. Stop looking at publications for things that you think sound smart to share and start looking at yourself.

i haven't read too far into it yet but what i said kind of reminds me of whitehead's book on symbolism where he states that the artist trains to undermine our usual capacity of inferring objects from impressions, in that they want to be quicker to identify and appreciate assemblages of form and color as opposed to only seeing "a chair" or something. as such it was rather unartistic of me to interpret the shadow in the order of a bundle of qualities -> an object in itself -> the quality of another object.

and while that in general matches up with what i think was happening i think my experience in its details is probably a little closer to what freud said in negation: presentations are first felt to us as reality and caused by objects; then we taken them as representations, detached from their causal objects; then we try to refind the original causal objects of those representations in externality.

but for freud presentations belong to the perception-consciousness system, and reality-testing on representations is done in the unconscious. perhaps the confusion i talked about originally is due to extended cathexis: the unconscious typically only samples from perception-consciousness periodically, and so extended sampling of external excitations due to, say, being unable to come to a judgment on them is due to a lack of certain qualities in the impressions and results in that sort of worry, fear, or anxiety that comes with being unable to recognize a thing definitively.