Bibliographic record
Abstract
One day some years ago at the Sexual Health Clinic where I worked a woman recently arrived from Poland came in asking for the contraceptive sponge. After some discussion about efficacy and gentle urging on my part to consider combining sponge and condom or even to consider methods with lower failure rates she assured me that this one was perfect for her. The main method she and her partner were using was withdrawal and the sponge was in fact her backup. My eyes widened (imperceptibly of course). Wouldnt she rather consider a method where he could actually ejaculate inside her--such as the pill for example? Not at all! was her reply. Withdrawal is our method. A man has to know how to pull out. Thats part of being a man. The sponge is just in case he doesnt. He wont know Im using it. When it comes time to make a baby he can stay inside! (excerpt)
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".