Constructing a longitudinal database of Targeted Regulation of Abortion Providers laws
Bibliographic record
Abstract
Objective: To build a longitudinal state-level database on targeted regulation of abortion provider (TRAP) laws.Data sources: Primary sources included state websites, Lexis Nexis Quicklaw, and WestlawNext.We also relied on a range of secondary sources (including media reports and existing cross-sectional data) to pinpoint policy enactment and enforcement. Study design:This was an iterative state-level review and compilation of data on TRAP shifts from 1973 to present.Data collection: Two coders captured quantitative and qualitative data on TRAP policy activity and timing, focusing specifically on ambulatory surgical center (ASC) laws, admitting privilege requirements, and transfer agreements as these policies may pose significant compliance challenges to providers.Data were repeatedly cross-referenced and ultimately compiled to build a comprehensive record of state-level TRAP shifts over time.Principal findings: According to our search results, 25 states had ever enacted an ASC, admitting privilege, or transfer agreement law.Fewer states (n=21) enforced these laws, and many currently face legal challenges.Conclusions: TRAP laws are favored by many states as a way to regulate abortion provision, but the lack of longitudinal data on state-level policy shifts hinders our ability to quantify the impact of these laws beyond a single-state setting.These data can be used to better understand the impact of TRAP laws.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".