Bibliographic record
Abstract
The conventional wisdom is that breast cancer devastates women’s lives, even when the disease is controlled by modern, multimodality treatments. We, the general public, take our stereotypes not so much from real life but from novels, movies of the week, and soap operas. Our poor heroine loses her breast (or at least gains an ugly scar that would turn off any but the most desperate man). Needless to say, her sex life falls apart. If she is single, her boyfriend leaves her. If she is married, she ends up divorced while her husband finds a younger partner who flaunts a perfect bosom in skimpy halter tops when our heroine picks up the kids for weekend visits. Of course we learn that our heroine the survivor only got breast cancer because of stress. In the last 3 years, she lost both her parents in a tragic plane crash, her teenaged son was arrested for marijuana possession, she supported her husband emotionally and financially when he was fired from his job for insider trading, and after fending off sexual advances from her boss, she was passed over for the job promotion she clearly deserved.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".