Bibliographic record
Abstract
National and international courts and tribunals are increasingly ruling that although states may aim to deter unlawful abortion by criminal penalties, they bear a parallel duty to inform physicians and patients of when abortion is lawful. The fear is that women are unjustly denied safe medical procedures to which they are legally entitled, because without such information physicians are deterred from involvement. With particular attention to the European Court of Human Rights, the UN Human Rights Committee, the Constitutional Court of Colombia, the Northern Ireland Court of Appeal, and the US Supreme Court, decisions are explained that show the responsibility of states to make rights to legal abortion transparent. Litigants are persuading judges to apply rights to reproductive health and human rights to require states' explanations of when abortion is lawful, and governments are increasingly inspired to publicize regulations or guidelines on when abortion will attract neither police nor prosecutors' scrutiny. This paper was co-authored with Rebecca Cook , University of Toronto ( rebecca.cook@utoronto.ca ) and Bernard Dickens , University of Toronto ( bernard.dickens@utoronto.ca ).
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 | 0.108 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".