Would it be feasible for European Union countries to implement Safe Access Zones for premises providing abortion services?
Bibliographic record
Abstract
Anti-abortion protestors situated near premises providing abortion services create barriers and hurdles to accessing abortion services, which violates the right of pregnant people to seek sexual and reproductive health services. There has been shown to be a need for Safe Access Zones (SAZs) to guarantee physical access to abortion services without obstruction. SAZs usually operate within a prescribed radius around premises providing abortion services and set out what behaviour is prohibited. The objective of this paper is to present a summary of the international experience of introducing and implementing SAZ laws, and to explain the lessons to be learned from this experience. SAZ legislation has been successfully enacted internationally in 22 jurisdictions (USA excluded). Countries with SAZ laws include Australia, New Zealand, the UK, and parts of Canada. Despite the Parliamentary Assembly of the Council of Europe calling for the introduction of SAZs in 2022, only two European Union (EU) countries have implemented this recommendation so far. On the basis of the medical and legal insights gained from the functioning of SAZs to date, it is the authors' opinion that it would be feasible for the 25 EU countries that do not yet have such zones to legislate for SAZs.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".