doing my best to only reblog verified GFM Campaigns for 🍉
C | later 20s | She/they pronouns | demi + bi, queer | Please tell me if you need things tagged! | Pictures of my face tagged 'me' or something about my hair...
Why is there a diarrhea parasite outbreak in America?
Because Donald Trump and his Republican allies cut funding to disease prevention and control.
Why is measles back in America?
Because Donald Trump and his Republican allies, including RFK Jr, cut vaccine and measles prevention funding and programs.
Why is the New World Screwworm infecting cattle in Texas, when it had previously been eliminated from the area?
Because Donald Trump and his Republican allies, including Elon Musk, cut USAID funding, which in part worked to monitor and prevent screwworm outbreaks.
Why was there a flu outbreak in our armed forces?
Because Donald Trump and his Republican allies, including Pete Hegseth, cut vaccination requirements for our armed forces, putting all of them at risk alongside our military readiness.
What contributed to the Covid outbreak?
Donald Trump and his Republican allies cut funding to a pandemic prevention program in his first term because Barack Obama had created it.
“America first” sends its regards. Trump voters, please learn your lesson. And if you don’t, it’ll be taught to you again with yet another diarrhea outbreak.
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"Six weeks into the term, I assigned my rhetoric and writing students a 20-page article. It was the same length I had assigned for five years and the same length I had read without complaint as an undergraduate a decade ago. Not one student finished it.
When I asked why, a student answered honestly: It was too long, and she kept losing track of what the paper was about. This was not a remedial class: These were students who had cleared the admissions process and written essays good enough to get them here. Yet a routine academic reading assignment had defeated them.
Every generation of professors has complained that their students cannot read. The lament is usually overblown, but data have caught up to anecdote, and what I am seeing in my classroom is no longer a hunch. There is a measurable, generational collapse in sustained reading and writing, and the academy is responding to it with improvisation and exhaustion rather than the structural overhaul it requires.
In February 2024, Adam Kotsko, who teaches in the Shimer Great Books School at North Central College, wrote in Slate that students who once handled 30 pages of reading per class meeting now seem “intimidated by anything over 10 pages and seem to walk away from readings of as little as 20 pages with no real understanding.” Crucially, he added that this is “not a matter of laziness on the part of the students” but of underlying skills they were never given a chance to build.
The Chronicle of Higher Education’s 2024 investigation found the same pattern across institutions as different as the Stevens Institute of Technology and Wellesley College, where the average SAT exceeds 1400. Nicholaus Gutierrez, an assistant professor at Wellesley, told The Chronicle that the baseline for what students consider a reasonable amount of work has dropped so noticeably that he has cut his readings accordingly; a 750-word essay now strikes many students as long. At Stevens, the science and technology studies associate professor Theresa MacPhail described following the mantra of “meet your students where they are” for so long that she has begun to feel “like a cruise director organizing games of shuffleboard.”
Worse, the national data tell the same story in colder language. On the 2011 National Assessment of Educational Progress (NAEP) writing assessment, which is the most recent comprehensive writing benchmark, only 24 percent of 12th graders reached the Proficient level, and just 3 percent reached Advanced; another 21 percent scored below Basic. The reading side of the ledger is worse, and getting worse fast: The 2024 NAEP results released in September 2025 show 12th-grade reading scores at the lowest level recorded since the assessment began in 1992. Thirty-two percent of 12th graders now score below NAEP Basic in reading, meaning that, in the assessment’s own language, they likely “cannot draw general conclusions based on concepts presented explicitly in a text.” And yet more than half of these same seniors reported being accepted to a four-year college. That last sentence is the whole problem in one line: We are admitting a cohort that cannot read at a college level and are pretending otherwise.
Why is this happening? One reason, of course, is smartphones.
I came into teaching as a skeptic of the anti-smartphone argument: I had a phone in my pocket throughout high school and college in the 2010s, and I read long books anyway. I now think I was wrong, because the neuroscience has caught up. In a 2017 paper, Adrian F. Ward and colleagues at the University of Texas at Austin’s McCombs School of Business showed that the mere presence of a participant’s smartphone — whether that be face down, powered off, untouched, or across the desk out of vision — measurably reduces available working memory and fluid intelligence on cognitive tests, with the largest effects on the most phone-dependent users. A 2022 study by Motoyasu Honma and colleagues at Japan’s Showa University used near-infrared spectroscopy to compare reading on a smartphone with reading the same passage on paper, and found that smartphone reading produced overactivity in the prefrontal cortex, suppressed sigh generation, and led to general lower comprehension scores; the authors argued that the sigh inhibition and prefrontal overload were causally linked to the comprehension decline.
So when a student tells me they “kept losing track” of a 20-page article, I have to acknowledge that they may be describing a measurable neurological condition. The neural pathways that support sustained attention are built by use, and they atrophy without it. Your body is a use-it-or-lose-it system, and the brain is no exception.
Another reason for the decline in student reading capability is increasing reliance on generative AI. In June 2025, Nataliya Kosmyna and colleagues at the MIT Media Lab released a preprint titled “Your Brain on ChatGPT.” They divided 54 participants into three groups writing SAT-style essays — one using ChatGPT, the second group using a search engine, the last group using nothing — and monitored brain activity with a 32-channel EEG. The ChatGPT group showed the lowest neural connectivity of the three, with up to 55 percent reduced connectivity compared with the brain-only group, and “consistently underperformed at neural, linguistic, and behavioral levels.” Eighty-three percent of LLM users could not quote a single line from essays they had written minutes earlier. When the LLM group was forced to write without AI in a follow-up session, their brain activity did not bounce back to baseline; the researchers coined the term “cognitive debt” for the lingering deficit.
This is the first neurophysiological evidence that early reliance on LLMs measurably alters the brain’s engagement with writing tasks, and it is consistent with what those of us in front of classrooms are watching happen in real time. When I assign analysis, I am not trying to extract a polished product; I am trying to put the student’s mind through resistance in order to make it stronger. Offloading the struggle to a chatbot does not “free students up for higher-order work.” It deprives them of building the strength to do any substantial cognitive work at all.
There is a final factor that is contributing to this decline in reading skills, and that is that the students arriving in my classroom today are the first cohort to have experienced Common Core-influenced reading instruction across the entirety of their K–12 schooling. Whatever the standards’ original intent, the on-the-ground implementation in many districts replaced sustained reading with the practice of pulling “evidence” from disconnected short passages, the same format used on the standardized tests that increasingly determine school funding. The education scholar Natalie Wexler, among others, has documented this pivot in detail: Students drilled on “finding the main idea” in two-paragraph excerpts never build the stamina or background knowledge that longform reading requires. The pandemic then added fuel to a fire that was already burning. NAEP scores for 13-year-olds dropped sharply in 2022 and have not recovered. A 2023 EdWeek survey found that 24 percent of secondary-school administrators described pandemic learning loss in English and language arts as “severe or very severe.”
In July 2025, the journalist Mary Harrington argued in The New York Times that “thinking is becoming a luxury good.” The ability to read deeply and reason at length is fragmenting along class lines as ultra-processed digital media replaces text in everyday life, much as ultra-processed food has replaced cooking. Her longer treatment of the subject in First Things makes the more provocative case that we are witnessing the end of print culture itself, and with it the end of the cognitive substrate on which modern liberal democracy was built.
I see this stratification in the classroom and on the page every week. My students from districts that protected sustained reading through small class sizes, strict phone policies, and faculty who refused to teach to the test all arrive with their attention relatively intact. My students from districts that surrendered to devices and standardized testing arrive cognitively winded. A democracy that requires a literate electorate is now training one fraction of that electorate out of literacy while marketing to the other a “deep work” lifestyle as a luxury good. The students who cannot read a 20-page article today are the voters who will not be able to read a bill, or the jurors who cannot follow a closing argument, tomorrow.
I do what I can in my own classroom to address the problems. I break 20-page articles into two halves and assign the first half with explicit analytical tasks. I require exploratory writing before formal drafts. I model (visibly, on the board) how to track an argument across pages or distinguish a source’s claim from my own analysis. I make structured peer review explicit, because the workshop format I used to take for granted now collapses into “this is good” and “maybe add more details” the moment I step back.
But I want to be plain about the limits of what an individual instructor can do, and all of these solutions have costs. Scaffolding a 20-page article into halves compromises the integrity of the argument I am asking students to engage, just as modeling note-taking in a credit-bearing rhetoric course is using a college slot to teach a middle-school skill. None of the syllabi I teach are designed to deliver this type of cognitive rehabilitation, and pretending otherwise has produced credential inflation. We cannot keep conferring degrees on students who cannot do what the degree is supposed to certify.
I’m afraid I don’t have answers. I do, however, have some questions that may point us in the right direction. If higher education is going to respond to the reading crisis as a structural problem rather than a private burden carried by composition instructors and adjuncts, it has to stop avoiding the following questions: If a majority of incoming students cannot read at a level the curriculum requires, are we admitting students we cannot serve, or offering a curriculum we cannot provide?
Why are first-year writing and reading-intensive general-education courses still the most adjunctified, lowest-paid, highest-load corner of the university, at the precise moment when their work has become the most important work the institution does? What is the responsible institutional response for AI usage: Is it a syllabus statement, or a sequencing principle that requires students to demonstrate the cognitive work themselves before AI assistance is permitted?
Why are most college classrooms still phone-permissive by default? K–12 districts from Florida to California are now banning phones bell to bell; higher education has somehow lagged behind the public schools. Universities benefit from a pipeline they did not build and refuse to repair. What would it mean for a university system to invest seriously in the reading instruction happening in the high schools that feed it, rather than treating remediation as something to be quietly outsourced to first-year composition instructors?
The thing I am no longer willing to do is pretend this is a temporary adjustment period, or that “students will adapt.” They will not adapt on their own. The conditions that produced this collapse are still in place: the phones, the algorithmic feeds, the test-prep excerpts, staffing models that load the reading-intensive work onto the most precarious faculty, and now the chatbots that finish students’ sentences before they’ve even begun to think of them. If we want literate citizens, we will have to rebuild the conditions for literacy deliberately, against the grain of every incentive currently pointed the other way. I know the academy has the will to do that. It also has the obligation."
— Tyler Jagt, 1 June 2026, "My Students Can’t Read"
The generational collapse in literacy is measurable, persistent, and likely to get worse.
If you're writing 18th century dialogue, this website lets you search words and phrases to double-check whether they were in use & meant what you intend. It doesn't include every period-accurate use of a word/phrase, but it certainly helped me separate genuine 18th century grammar from the vague tangle of 💬old-fashioned fancy-speak💬 I've internalized from TV and video games.
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Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
The people who offload all this thought. Sone of them are engineers. Lawyers. Safety inspectors. People in charge of logistics and supply chains. Entire jobs are being replaced by glorified autocomplete which doesn't have a concept of 'truth.'
We're going to start seeing the shit bubbling to the surface soon if this doesn't change. Entire industries could fail.
🚨BREAKING: OpenAI published a paper proving that ChatGPT will always make things up.
Not sometimes. Not until the next update. Always. They proved it with math.
Even with perfect training data and unlimited computing power, AI models will still confidently tell you things that are completely false. This isn't a bug they're working on. It's baked into how these systems work at a fundamental level.
And their own numbers are brutal. OpenAI's o1 reasoning model hallucinates 16% of the time. Their newer o3 model? 33%. Their newest o4-mini? 48%. Nearly half of what their most recent model tells you could be fabricated. The "smarter" models are actually getting worse at telling the truth.
Here's why it can't be fixed. Language models work by predicting the next word based on probability. When they hit something uncertain, they don't pause. They don't flag it. They guess. And they guess with complete confidence, because that's exactly what they were trained to do.
The researchers looked at the 10 biggest AI benchmarks used to measure how good these models are. 9 out of 10 give the same score for saying "I don't know" as for giving a completely wrong answer: zero points. The entire testing system literally punishes honesty and rewards guessing.
So the AI learned the optimal strategy: always guess. Never admit uncertainty. Sound confident even when you're making it up.
OpenAI's proposed fix? Have ChatGPT say "I don't know" when it's unsure. Their own math shows this would mean roughly 30% of your questions get no answer. Imagine asking ChatGPT something three times out of ten and getting "I'm not confident enough to respond." Users would leave overnight. So the fix exists, but it would kill the product.
This isn't just OpenAI's problem. DeepMind and Tsinghua University independently reached the same conclusion. Three of the world's top AI labs, working separately, all agree: this is permanent.
Every time ChatGPT gives you an answer, ask yourself: is this real, or is it just a confident guess?
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since snap benefits are being threatened i wanna share the single resource i can atm which is the woman behind Dollar Tree Dinners. for years now she has consistently provided filling, healthy recipes that really push the envelope on good meals from dollar tree ingredients. especially since next month will involve lots of family meals regardless if you celebrate the holiday, people should be pleased to know she puts together videos showing how to make a holiday meal on a budget.
incredibly bizarre and confusing seeing ppl call themselves "chuds" all the sudden b/c like
thats what we call neo nazis and shitty conservative bros? or at least its what we used to call them? why are ppl calling themselves "chuds" affectionately now
what is happening
yall know chud means fascist right like please tell me yall know that
im hoping this is a case of "younger folks on the internet adopting Silly Word b/c its Silly and not realizing it actually means something"
so here's me educating! you're calling yourselves fascists! thats what you're doing! maybe don't do that and use your head before you start using every goofy word you see!
legit the best advice i can give you: feed your friends
any time someone is in any kind of crisis or upheaval, offer to feed them. tell them they don't have to choose what it is if they can't make decisions, just ask about allergies and preferences and tell them you're just gonna make food happen at their house.
friend having a baby? delivery gift certificate to order food to the hospital after the kid shows up.
someone's relative passes away? offer to make them dinner.
buddy gets laid off? ask if you can order them lunch.
pal stuck in a depressive episode? offer to drive them to fucking mcdonalds, if that's what they want.
people in crisis are tired and sad and angry and the last thing most of them are doing is thinking about feeding themselves. so if you have the ability or time or money, providing that is always, always a good move.
legit i do this all the time, and it is 100% always appreciated. i have taught all my friends that when something happens, we feed each other. it makes people feel extremely cared for, and I cannot recommend it enough.
Right after I reblogged this I found out a friends’ mom was hospitalized — this is such a good reminder
If you can, provide disposable plates/utensils and save them from doing dishes — when my husband was diagnosed with cancer, bulk paper products were our most appreciated gift
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