#quantitative survey

[kdsburneraccount]

So the NFLPA released report cards for each team's working conditions that graded them based on polls from players, which I found pretty interesting but also a little depressing. You can read it here: NFL Player Team Report Cards | NFLPA

More thoughts under the cut since I was initially going to put my thoughts in the tags but I thought reading it was quite eye-opening.

The Bengals being graded an F- for nutrition... I'm not that surprised considering what former players have said about the cafeteria, but it's still pretty embarrassing. And those comments were from the 2000s. Not providing dinner, vitamins, or supplements to players is a little questionable, especially if players are going to be spending long days at practice. And the fact that they’ve had breastfeeding mothers sit on the floor of public restrooms to take care of their kids is… concerning. (This is a Mike Brown moment. Unfortunately. The “no indoor practice facility” jokes looking more and more true by the moment. Like I get he’s pretty poor for an NFL owner but your team made the Super Bowl two seasons ago invest in the facilities more have an actual food service on hand cmon man why are you proving Carson Palmer right)

I wasn't surprised looking at the Falcons' grading of their strength coach (by and large, strength coaches were graded exceedingly positively by the players, except for the Falcons and the Ravens. In the case of the Ravens it appeared that they had a strength and conditioning coach who was pretty disliked by the players, and that coach did get fired, so at least the team's aware of it), mostly bc the Falcons aren't very good at tackling 😭 hopefully they make a change in that regard because man. At least they think Arthur Blank is ok.

Haven't watched mid-season Hard Knocks so didn't really have an idea of how the Cardinals' facilities were, but... wow. Overall second to last behind the Commanders (who are, y'know, dysfunctional), which kinda surprised me but at the same time the Cardinals are kind of low-key dysfunctional now I think about that. Making players pay for dinner out of their payroll... don't like that! Oh yeah and their training facilities are apparently a health and safety risk (fun).

It is interesting to note that the rankings of the facilities didn't necessarily correlate with team success: the top-ranked teams in this survey were the Vikings, Dolphins, Raiders, Texans, and Cowboys while the bottom-5 teams were the Commanders, Cardinals, Chargers, Chiefs, and Jaguars (Bengals dodging this phew). This is probably because a lot of stuff that's surveyed here can impact team performance, but only if it's egregiously bad (ie Cardinals or Commanders). Was surprised by how the Chiefs graded out on their training staff considering they just won a Super Bowl but I would wager that's to do with Andy Reid and his whole system; works but isn't the nicest about it.

I thought that the way some categories were weighted were a little questionable, ie travel and treatment of families being abt the same level. Personally having younger players room with each other is whatever, not having a proper space for families is a bigger issue. But maybe that's me being unsympathetic (and there is the whole difference between a star player and practice squad guy to consider because their treatment would be different).

Overall, pretty good survey, I do hope it's able to enact some awareness bc the NLFPA isn't that strong, but they do seem to be doing their best as a union (working in the interest of players).

[insighttellers]

Precision Insights: Expert Quantitative Market Research Services

Our Quantitative Market Research Services help you quickly gather insights from our panellists and understand the changing consumer behaviour. Using our comprehensive services, we find the answers to the most of your questions! Follow this link to know more https://insighttellers.com/services/quantitative-research-market

[academicelephant]

Quantitative research is fun. Or at least creating surveys is, it remains to be seen if processing the data will be too

[notayesmanseconomics]

The ECB is trying to reduce what is already weak money supply growth

These are awkward times for the ECB and its President Christine Lagarde. There are obvious issues in her home country of France which is at the heart of the Euro project. But my main issue is her policy of abandoning Forward Guidance on interest-rates and then promising an interest-rate cut in June. This came with the implication that we were moving into a sequence of cuts with another next…

[dayofbanks]

Monetary Policy and the Evolution of Wealth Disparity - An Assessment Using US Survey of Consumer Finance Data.

This session examines the distributional effects of recent - monetary policies on income and wealth. Using the Federal Reserve Board's Survey of Consumer Finances, the research tracks key subpopulations as monetary policy shifted from conventional interest rates to Quantitative Easing. Employing advanced modeling techniques, the study analyzes volatility and bifurcation in capital gains and incomes among U.S.

Watch the Monetary Policy and the Evolution of Wealth Disparity - An Assessment Using US Survey of Consumer Finance Data!

[marketxcel]

5 Methods of Data Collection for Quantitative Research

Discover five powerful techniques for gathering quantitative data in research, essential for uncovering trends, patterns, and correlations. Explore proven methodologies that empower researchers to collect and analyze data effectively.

[philomathresearch]

The Power of Precision: A Deep Dive into Quantitative Data Research

In the dynamic world of research and decision-making, data is the bedrock upon which sound strategies are built. Among the various research methodologies, quantitative data research stands tall as a powerful and precise tool. At Philomath Research, we understand the significance of quantitative research and its unparalleled ability to uncover insights, drive informed decisions, and shape the future. In this deep-dive exploration, we'll delve into the world of quantitative data research, understanding its methodologies, applications, and the transformative impact it has on businesses and industries.

Chapter 1: Understanding Quantitative Data Research

Quantitative data research is a systematic approach to collecting, analyzing, and interpreting numerical data. It involves structured data collection methods such as surveys, questionnaires, experiments, and observations. This methodology ensures that data is precise, measurable, and statistically significant. The quantitative approach allows researchers to answer questions with numbers, providing a foundation for data-driven decision-making.

Chapter 2: The Methodologies of Quantitative Research

Quantitative research methodologies come in various forms, each tailored to the specific research objectives. We'll explore the most common methods:

Surveys and Questionnaires: Surveys are a staple of quantitative research, enabling researchers to collect data from a large sample size. We'll delve into best practices for designing effective surveys and questionnaires.

Experiments: Controlled experiments allow researchers to isolate variables and establish causal relationships. We'll discuss how experiments are designed and executed.

Observational Research: Observational studies involve systematic observations of subjects in their natural settings. We'll explore the advantages and challenges of this method.

Chapter 3: The Power of Precision in Data Collection

The heart of quantitative research lies in its precision in data collection. We'll explore:

Structured Data Collection: Quantitative research employs structured data collection tools that ensure uniformity and consistency in responses.

Large Sample Sizes: The ability to collect data from large sample sizes provides statistical power and reliability.

Quantifiable Data: Numerical data is highly quantifiable and lends itself to statistical analysis.

Statistical Significance: Quantitative research allows researchers to determine the statistical significance of findings, providing confidence in the results.

Chapter 4: Data Analysis in Quantitative Research

In this chapter, we'll dive into the data analysis phase of quantitative research:

Descriptive Statistics: Descriptive statistics provide an overview of the data, including measures of central tendency and dispersion.

Inferential Statistics: Inferential statistics allow researchers to draw conclusions about a population based on a sample. We'll explore hypothesis testing, confidence intervals, and more.

Statistical Software: The role of statistical software in data analysis, including popular tools like SPSS, SAS, and R.

Chapter 5: The Real-World Applications of Quantitative Research

Quantitative research finds application in a wide range of fields and industries. We'll showcase some real-world examples:

Market Research: How businesses use quantitative research to understand consumer behavior, preferences, and market trends.

Healthcare: The role of quantitative research in clinical trials, patient outcomes analysis, and healthcare policy.

Education: How educational institutions leverage quantitative research to improve teaching methods and student performance.

Finance: Quantitative research in financial modeling, risk assessment, and investment strategies.

Chapter 6: The Transformative Impact of Quantitative Research

Quantitative research has the power to transform industries and shape strategies. We'll discuss:

Evidence-Based Decision-Making: How quantitative research provides the evidence needed for informed decisions.

Competitive Advantage: How businesses gain a competitive edge by using quantitative insights to optimize operations and customer experiences.

Innovation: The role of quantitative research in driving innovation, product development, and process improvements.

Chapter 7: Challenges and Ethical Considerations

No research methodology is without its challenges and ethical considerations. We'll explore:

Sampling Bias: The potential for bias in sampling methods and how researchers mitigate it.

Privacy and Data Security: The importance of safeguarding participant data and ensuring compliance with data protection regulations.

Ethical Conduct: The ethical responsibilities of researchers in quantitative studies, including informed consent and transparency.

Chapter 8: The Future of Quantitative Data Research

As technology advances and the world becomes more data-centric, we'll look ahead to the future of quantitative research:

Big Data and AI: The convergence of quantitative research with big data analytics and artificial intelligence.

Multimodal Research: The potential for combining quantitative and qualitative research methods for deeper insights.

Globalization: How quantitative research is adapting to the challenges and opportunities of a globalized world.

Conclusion: Harnessing the Power of Precision

Quantitative data research is not merely a methodology; it's a strategic imperative in today's data-driven world. At Philomath Research, we embrace the power of precision in data collection and analysis. We understand that the insights derived from quantitative research have the potential to transform businesses, industries, and societies.

In this deep dive into quantitative data research, we've explored its methodologies, applications, and transformative impact. It's a journey that leads to a deeper understanding of the world around us, enabling us to make informed decisions and shape a brighter future.

To learn more about how Philomath Research leverages the power of precision in quantitative data research, please visit www.philomathresearch.com. Together, we can uncover insights that drive success and innovation.

[researchers-me]

Discover the qualitative and quantitative surveys to understand your target audience's needs. Our quantitative research collects large amounts of data for informed decision-making.

[electronalytics]

NB-IoT Smart Meter Market

[market-research-consultancy]

Quantitative Market Research - Track Opinion

Track Opinion provides quantitative market research services to help businesses make informed decisions. We use surveys, polls, and other data collection methods to gather insights into customer behavior, preferences, and attitudes. Our team of experienced researchers can help you design, implement, and analyze your quantitative market research project.

[treebreadcares]

Factors influence Parkinson Disease Patients HRQoL in Ireland

Factors influence Parkinson Disease Patients HRQoL in Ireland

As the global population continues to age, the financial and public health impacts of Parkinson's disease are becoming more relevant. This study aimed to investigate the factors influencing Health-Related Quality of Life (HRQoL) for people with Parkinson’s in one region of Ireland. Read the full article

[academicelephant]

We have access to Webropol on behalf of the university (at least at this point anyway) but I was wondering what it would cost if one wanted it for personal use and it turned out that for 12 months it would cost at least 1 000 € but it can go up to 40 000 €. Thank God we don’t need to pay it ourselves!

[mostlysignssomeportents]

AI “art” and uncanniness

TOMORROW (May 14), I'm on a livecast about AI AND ENSHITTIFICATION with TIM O'REILLY; on TOMORROW (May 15), I'm in NORTH HOLLYWOOD for a screening of STEPHANIE KELTON'S FINDING THE MONEY; FRIDAY (May 17), I'm at the INTERNET ARCHIVE in SAN FRANCISCO to keynote the 10th anniversary of the AUTHORS ALLIANCE.

When it comes to AI art (or "art"), it's hard to find a nuanced position that respects creative workers' labor rights, free expression, copyright law's vital exceptions and limitations, and aesthetics.

I am, on balance, opposed to AI art, but there are some important caveats to that position. For starters, I think it's unequivocally wrong – as a matter of law – to say that scraping works and training a model with them infringes copyright. This isn't a moral position (I'll get to that in a second), but rather a technical one.

Break down the steps of training a model and it quickly becomes apparent why it's technically wrong to call this a copyright infringement. First, the act of making transient copies of works – even billions of works – is unequivocally fair use. Unless you think search engines and the Internet Archive shouldn't exist, then you should support scraping at scale:

https://pluralistic.net/2023/09/17/how-to-think-about-scraping/

And unless you think that Facebook should be allowed to use the law to block projects like Ad Observer, which gathers samples of paid political disinformation, then you should support scraping at scale, even when the site being scraped objects (at least sometimes):

https://pluralistic.net/2021/08/06/get-you-coming-and-going/#potemkin-research-program

After making transient copies of lots of works, the next step in AI training is to subject them to mathematical analysis. Again, this isn't a copyright violation.

Making quantitative observations about works is a longstanding, respected and important tool for criticism, analysis, archiving and new acts of creation. Measuring the steady contraction of the vocabulary in successive Agatha Christie novels turns out to offer a fascinating window into her dementia:

https://www.theguardian.com/books/2009/apr/03/agatha-christie-alzheimers-research

Programmatic analysis of scraped online speech is also critical to the burgeoning formal analyses of the language spoken by minorities, producing a vibrant account of the rigorous grammar of dialects that have long been dismissed as "slang":

https://www.researchgate.net/publication/373950278_Lexicogrammatical_Analysis_on_African-American_Vernacular_English_Spoken_by_African-Amecian_You-Tubers

Since 1988, UCL Survey of English Language has maintained its "International Corpus of English," and scholars have plumbed its depth to draw important conclusions about the wide variety of Englishes spoken around the world, especially in postcolonial English-speaking countries:

https://www.ucl.ac.uk/english-usage/projects/ice.htm

The final step in training a model is publishing the conclusions of the quantitative analysis of the temporarily copied documents as software code. Code itself is a form of expressive speech – and that expressivity is key to the fight for privacy, because the fact that code is speech limits how governments can censor software:

https://www.eff.org/deeplinks/2015/04/remembering-case-established-code-speech/

Are models infringing? Well, they certainly can be. In some cases, it's clear that models "memorized" some of the data in their training set, making the fair use, transient copy into an infringing, permanent one. That's generally considered to be the result of a programming error, and it could certainly be prevented (say, by comparing the model to the training data and removing any memorizations that appear).

Not every seeming act of memorization is a memorization, though. While specific models vary widely, the amount of data from each training item retained by the model is very small. For example, Midjourney retains about one byte of information from each image in its training data. If we're talking about a typical low-resolution web image of say, 300kb, that would be one three-hundred-thousandth (0.0000033%) of the original image.

Typically in copyright discussions, when one work contains 0.0000033% of another work, we don't even raise the question of fair use. Rather, we dismiss the use as de minimis (short for de minimis non curat lex or "The law does not concern itself with trifles"):

https://en.wikipedia.org/wiki/De_minimis

Busting someone who takes 0.0000033% of your work for copyright infringement is like swearing out a trespassing complaint against someone because the edge of their shoe touched one blade of grass on your lawn.

But some works or elements of work appear many times online. For example, the Getty Images watermark appears on millions of similar images of people standing on red carpets and runways, so a model that takes even in infinitesimal sample of each one of those works might still end up being able to produce a whole, recognizable Getty Images watermark.

The same is true for wire-service articles or other widely syndicated texts: there might be dozens or even hundreds of copies of these works in training data, resulting in the memorization of long passages from them.

This might be infringing (we're getting into some gnarly, unprecedented territory here), but again, even if it is, it wouldn't be a big hardship for model makers to post-process their models by comparing them to the training set, deleting any inadvertent memorizations. Even if the resulting model had zero memorizations, this would do nothing to alleviate the (legitimate) concerns of creative workers about the creation and use of these models.

So here's the first nuance in the AI art debate: as a technical matter, training a model isn't a copyright infringement. Creative workers who hope that they can use copyright law to prevent AI from changing the creative labor market are likely to be very disappointed in court:

https://www.hollywoodreporter.com/business/business-news/sarah-silverman-lawsuit-ai-meta-1235669403/

But copyright law isn't a fixed, eternal entity. We write new copyright laws all the time. If current copyright law doesn't prevent the creation of models, what about a future copyright law?

Well, sure, that's a possibility. The first thing to consider is the possible collateral damage of such a law. The legal space for scraping enables a wide range of scholarly, archival, organizational and critical purposes. We'd have to be very careful not to inadvertently ban, say, the scraping of a politician's campaign website, lest we enable liars to run for office and renege on their promises, while they insist that they never made those promises in the first place. We wouldn't want to abolish search engines, or stop creators from scraping their own work off sites that are going away or changing their terms of service.

Now, onto quantitative analysis: counting words and measuring pixels are not activities that you should need permission to perform, with or without a computer, even if the person whose words or pixels you're counting doesn't want you to. You should be able to look as hard as you want at the pixels in Kate Middleton's family photos, or track the rise and fall of the Oxford comma, and you shouldn't need anyone's permission to do so.

Finally, there's publishing the model. There are plenty of published mathematical analyses of large corpuses that are useful and unobjectionable. I love me a good Google n-gram:

https://books.google.com/ngrams/graph?content=fantods%2C+heebie-jeebies&year_start=1800&year_end=2019&corpus=en-2019&smoothing=3

And large language models fill all kinds of important niches, like the Human Rights Data Analysis Group's LLM-based work helping the Innocence Project New Orleans' extract data from wrongful conviction case files:

https://hrdag.org/tech-notes/large-language-models-IPNO.html

So that's nuance number two: if we decide to make a new copyright law, we'll need to be very sure that we don't accidentally crush these beneficial activities that don't undermine artistic labor markets.

This brings me to the most important point: passing a new copyright law that requires permission to train an AI won't help creative workers get paid or protect our jobs.

Getty Images pays photographers the least it can get away with. Publishers contracts have transformed by inches into miles-long, ghastly rights grabs that take everything from writers, but still shifts legal risks onto them:

https://pluralistic.net/2022/06/19/reasonable-agreement/

Publishers like the New York Times bitterly oppose their writers' unions:

https://actionnetwork.org/letters/new-york-times-stop-union-busting

These large corporations already control the copyrights to gigantic amounts of training data, and they have means, motive and opportunity to license these works for training a model in order to pay us less, and they are engaged in this activity right now:

https://www.nytimes.com/2023/12/22/technology/apple-ai-news-publishers.html

Big games studios are already acting as though there was a copyright in training data, and requiring their voice actors to begin every recording session with words to the effect of, "I hereby grant permission to train an AI with my voice" and if you don't like it, you can hit the bricks:

https://www.vice.com/en/article/5d37za/voice-actors-sign-away-rights-to-artificial-intelligence

If you're a creative worker hoping to pay your bills, it doesn't matter whether your wages are eroded by a model produced without paying your employer for the right to do so, or whether your employer got to double dip by selling your work to an AI company to train a model, and then used that model to fire you or erode your wages:

https://pluralistic.net/2023/02/09/ai-monkeys-paw/#bullied-schoolkids

Individual creative workers rarely have any bargaining leverage over the corporations that license our copyrights. That's why copyright's 40-year expansion (in duration, scope, statutory damages) has resulted in larger, more profitable entertainment companies, and lower payments – in real terms and as a share of the income generated by their work – for creative workers.

As Rebecca Giblin and I write in our book Chokepoint Capitalism, giving creative workers more rights to bargain with against giant corporations that control access to our audiences is like giving your bullied schoolkid extra lunch money – it's just a roundabout way of transferring that money to the bullies:

https://pluralistic.net/2022/08/21/what-is-chokepoint-capitalism/

There's an historical precedent for this struggle – the fight over music sampling. 40 years ago, it wasn't clear whether sampling required a copyright license, and early hip-hop artists took samples without permission, the way a horn player might drop a couple bars of a well-known song into a solo.

Many artists were rightfully furious over this. The "heritage acts" (the music industry's euphemism for "Black people") who were most sampled had been given very bad deals and had seen very little of the fortunes generated by their creative labor. Many of them were desperately poor, despite having made millions for their labels. When other musicians started making money off that work, they got mad.

In the decades that followed, the system for sampling changed, partly through court cases and partly through the commercial terms set by the Big Three labels: Sony, Warner and Universal, who control 70% of all music recordings. Today, you generally can't sample without signing up to one of the Big Three (they are reluctant to deal with indies), and that means taking their standard deal, which is very bad, and also signs away your right to control your samples.

So a musician who wants to sample has to sign the bad terms offered by a Big Three label, and then hand $500 out of their advance to one of those Big Three labels for the sample license. That $500 typically doesn't go to another artist – it goes to the label, who share it around their executives and investors. This is a system that makes every artist poorer.

But it gets worse. Putting a price on samples changes the kind of music that can be economically viable. If you wanted to clear all the samples on an album like Public Enemy's "It Takes a Nation of Millions To Hold Us Back," or the Beastie Boys' "Paul's Boutique," you'd have to sell every CD for $150, just to break even:

https://memex.craphound.com/2011/07/08/creative-license-how-the-hell-did-sampling-get-so-screwed-up-and-what-the-hell-do-we-do-about-it/

Sampling licenses don't just make every artist financially worse off, they also prevent the creation of music of the sort that millions of people enjoy. But it gets even worse. Some older, sample-heavy music can't be cleared. Most of De La Soul's catalog wasn't available for 15 years, and even though some of their seminal music came back in March 2022, the band's frontman Trugoy the Dove didn't live to see it – he died in February 2022:

https://www.vulture.com/2023/02/de-la-soul-trugoy-the-dove-dead-at-54.html

This is the third nuance: even if we can craft a model-banning copyright system that doesn't catch a lot of dolphins in its tuna net, it could still make artists poorer off.

Back when sampling started, it wasn't clear whether it would ever be considered artistically important. Early sampling was crude and experimental. Musicians who trained for years to master an instrument were dismissive of the idea that clicking a mouse was "making music." Today, most of us don't question the idea that sampling can produce meaningful art – even musicians who believe in licensing samples.

Having lived through that era, I'm prepared to believe that maybe I'll look back on AI "art" and say, "damn, I can't believe I never thought that could be real art."

But I wouldn't give odds on it.

I don't like AI art. I find it anodyne, boring. As Henry Farrell writes, it's uncanny, and not in a good way:

https://www.programmablemutter.com/p/large-language-models-are-uncanny

Farrell likens the work produced by AIs to the movement of a Ouija board's planchette, something that "seems to have a life of its own, even though its motion is a collective side-effect of the motions of the people whose fingers lightly rest on top of it." This is "spooky-action-at-a-close-up," transforming "collective inputs … into apparently quite specific outputs that are not the intended creation of any conscious mind."

Look, art is irrational in the sense that it speaks to us at some non-rational, or sub-rational level. Caring about the tribulations of imaginary people or being fascinated by pictures of things that don't exist (or that aren't even recognizable) doesn't make any sense. There's a way in which all art is like an optical illusion for our cognition, an imaginary thing that captures us the way a real thing might.

But art is amazing. Making art and experiencing art makes us feel big, numinous, irreducible emotions. Making art keeps me sane. Experiencing art is a precondition for all the joy in my life. Having spent most of my life as a working artist, I've come to the conclusion that the reason for this is that art transmits an approximation of some big, numinous irreducible emotion from an artist's mind to our own. That's it: that's why art is amazing.

AI doesn't have a mind. It doesn't have an intention. The aesthetic choices made by AI aren't choices, they're averages. As Farrell writes, "LLM art sometimes seems to communicate a message, as art does, but it is unclear where that message comes from, or what it means. If it has any meaning at all, it is a meaning that does not stem from organizing intention" (emphasis mine).

Farrell cites Mark Fisher's The Weird and the Eerie, which defines "weird" in easy to understand terms ("that which does not belong") but really grapples with "eerie."

For Fisher, eeriness is "when there is something present where there should be nothing, or is there is nothing present when there should be something." AI art produces the seeming of intention without intending anything. It appears to be an agent, but it has no agency. It's eerie.

Fisher talks about capitalism as eerie. Capital is "conjured out of nothing" but "exerts more influence than any allegedly substantial entity." The "invisible hand" shapes our lives more than any person. The invisible hand is fucking eerie. Capitalism is a system in which insubstantial non-things – corporations – appear to act with intention, often at odds with the intentions of the human beings carrying out those actions.

So will AI art ever be art? I don't know. There's a long tradition of using random or irrational or impersonal inputs as the starting point for human acts of artistic creativity. Think of divination:

https://pluralistic.net/2022/07/31/divination/

Or Brian Eno's Oblique Strategies:

http://stoney.sb.org/eno/oblique.html

I love making my little collages for this blog, though I wouldn't call them important art. Nevertheless, piecing together bits of other peoples' work can make fantastic, important work of historical note:

https://www.johnheartfield.com/John-Heartfield-Exhibition/john-heartfield-art/famous-anti-fascist-art/heartfield-posters-aiz

Even though painstakingly cutting out tiny elements from others' images can be a meditative and educational experience, I don't think that using tiny scissors or the lasso tool is what defines the "art" in collage. If you can automate some of this process, it could still be art.

Here's what I do know. Creating an individual bargainable copyright over training will not improve the material conditions of artists' lives – all it will do is change the relative shares of the value we create, shifting some of that value from tech companies that hate us and want us to starve to entertainment companies that hate us and want us to starve.

As an artist, I'm foursquare against anything that stands in the way of making art. As an artistic worker, I'm entirely committed to things that help workers get a fair share of the money their work creates, feed their families and pay their rent.

I think today's AI art is bad, and I think tomorrow's AI art will probably be bad, but even if you disagree (with either proposition), I hope you'll agree that we should be focused on making sure art is legal to make and that artists get paid for it.

Just because copyright won't fix the creative labor market, it doesn't follow that nothing will. If we're worried about labor issues, we can look to labor law to improve our conditions. That's what the Hollywood writers did, in their groundbreaking 2023 strike:

https://pluralistic.net/2023/10/01/how-the-writers-guild-sunk-ais-ship/

Now, the writers had an advantage: they are able to engage in "sectoral bargaining," where a union bargains with all the major employers at once. That's illegal in nearly every other kind of labor market. But if we're willing to entertain the possibility of getting a new copyright law passed (that won't make artists better off), why not the possibility of passing a new labor law (that will)? Sure, our bosses won't lobby alongside of us for more labor protection, the way they would for more copyright (think for a moment about what that says about who benefits from copyright versus labor law expansion).

But all workers benefit from expanded labor protection. Rather than going to Congress alongside our bosses from the studios and labels and publishers to demand more copyright, we could go to Congress alongside every kind of worker, from fast-food cashiers to publishing assistants to truck drivers to demand the right to sectoral bargaining. That's a hell of a coalition.

And if we do want to tinker with copyright to change the way training works, let's look at collective licensing, which can't be bargained away, rather than individual rights that can be confiscated at the entrance to our publisher, label or studio's offices. These collective licenses have been a huge success in protecting creative workers:

https://pluralistic.net/2023/02/26/united-we-stand/

Then there's copyright's wildest wild card: The US Copyright Office has repeatedly stated that works made by AIs aren't eligible for copyright, which is the exclusive purview of works of human authorship. This has been affirmed by courts:

https://pluralistic.net/2023/08/20/everything-made-by-an-ai-is-in-the-public-domain/

Neither AI companies nor entertainment companies will pay creative workers if they don't have to. But for any company contemplating selling an AI-generated work, the fact that it is born in the public domain presents a substantial hurdle, because anyone else is free to take that work and sell it or give it away.

Whether or not AI "art" will ever be good art isn't what our bosses are thinking about when they pay for AI licenses: rather, they are calculating that they have so much market power that they can sell whatever slop the AI makes, and pay less for the AI license than they would make for a human artist's work. As is the case in every industry, AI can't do an artist's job, but an AI salesman can convince an artist's boss to fire the creative worker and replace them with AI:

https://pluralistic.net/2024/01/29/pay-no-attention/#to-the-little-man-behind-the-curtain

They don't care if it's slop – they just care about their bottom line. A studio executive who cancels a widely anticipated film prior to its release to get a tax-credit isn't thinking about artistic integrity. They care about one thing: money. The fact that AI works can be freely copied, sold or given away may not mean much to a creative worker who actually makes their own art, but I assure you, it's the only thing that matters to our bosses.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/05/13/spooky-action-at-a-close-up/#invisible-hand

[poupon]

🦆 it

which of these pet names is your OC most likely to call their romantic companion

Reblog for sample size or whatever this isn't a quantitative survey so do what you want I'm not the boss of you

[marketxcel]

What Is Market Research: Methods, Types & Examples

Learn about the fundamentals of market research, including various methods, types, and real-life examples. Discover how market research can benefit your business and gain insights into consumer behavior, trends, and preferences.