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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".