Access to knowledge and the Global Abortion Policies Database
Bibliographic record
Abstract
Research shows that women, healthcare providers, and even policy makers worldwide have limited or inaccurate knowledge of the abortion law and policies in their country. These knowledge gaps sometimes stem from the vague and broad terms of the law, which breed uncertainty and even conflict when unaccompanied by accessible regulation or guidelines. Inconsistency across national law and policy further impedes safe and evidence-based practice. This lack of transparency creates a crisis of accountability. Those seeking care cannot know their legal entitlements, service providers cannot practice with legal protection, and governments can escape legal responsibility for the adverse effects of their laws. This is the context for the newly launched Global Abortion Policies Database-an open-access repository that seeks to promote transparency and state accountability by providing clear and comprehensive information about national laws, policies, health standards, and guidelines, and by creating the capacity for comparative analysis and cross-referencing to health indicators, WHO recommendations, and human rights standards.
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 | 0.011 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.025 |
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".