Abortion Governance in Legal Permissive Frameworks: The promise of abortion decriminalization
Bibliographic record
Abstract
The literature on abortion politics closely follows trends in legislative change, celebrating the victories of feminist social movements and warning against potential backsliding. While most countries have some type of law governing abortion, rarely does the abortion literature consider which legal frameworks would be best at ensuring abortion access. This thesis addresses this gap by comparing legal frameworks from countries that demonstrate a desire to allow access to abortion care, that is Australia, Canada and Ireland. Through a normative comparative case study analysis and process tracing, I determined two metrics to evaluate access to abortion care: accessibility and stigma. As a result, this thesis argues that, compared to abortion liberalization and partial decriminalization, full abortion decriminalization, based on the Canadian case, is the most promising regulatory framework to guarantee abortion access. This thesis not only contributes meaningfully to academic debates but also offers actionable insights for policymakers and advocates seeking to expand reproductive rights globally. Canada’s experience, though unique, shows potential to enhance abortion access without direct legislative intervention, effectively challenging the traditional legal paradigm
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".