Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The language my friends and I are using to describe the outcome of Tuesday's elections combines astonishment, outrage, and a large dose of despair. Astonishment that a convicted felon, a twice impeached politician, a vicious misogynist, a racist, homophobe, liar, and cheat, who is a stated enemy of democracy, managed to secure the popular vote for a Republican presidential candidate for the first time since 2004 and—against so many predictions—win all of the states required to carry the electoral college by a significant margin. Along with his victory came control of the Senate and also—at this moment—likely the House of Representatives. The Supreme Court is already stacked with his allies, having ruled in his favor in many of the legal cases brought against his last acts as president. All branches of the government, in other words, are now in the hands of the neo-fascists who have extensive plans to dismantle what remains of American democracy. And make no mistake, they will implement their Project 2025. Even those of us in relatively comfortable positions, who live in “blue” states, will feel the impact of the abolition of the Department of Education; the assignment of health services to the vaccine-denying Robert Kennedy Jr.; the cost-cutting mania of Elon Musk; and the deregulation of what climate controls have managed to be implemented during the Biden administration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it