Bibliographic record
Abstract
As I sit down to write this, it’s been exactly one year since the New York Times broke the story of decades of sexual assault allegations by Hollywood mogul Harvey Weinstein. I know, because I’ve been glued to the Times website, like many women in North America and around the world, waiting to hear if the Senate will vote to confirm Republican Supreme Court nominee Brett Kavanaugh. Kavanaugh gave an extraordinary performance of White male self-entitlement before the Senate judiciary committee convened to grill him about accusations of sexual misconduct; this came hot on the heels of Dr. Christine Blasey Ford’s historic own testimony, in which she detailed her painful memories of his assault on her, and patiently taught the assembled Senate panelists how human psychology works, and how trauma is retained in the brain.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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".