Does Increased Abortion Lead to Reduced Crime? Evaluating the Relationship between Crime, Abortion, and Fertility
Bibliographic record
Abstract
Donohue and Levitt (2001) attribute over half the current decline in U.S. crime rates to the legalization of abortion. I contribute to the literature by using provincial Canadian data, which permits the segregation of trends in teenage abortions from general abortion rates. This distinction is important, as I find that a much larger drop in violent crime (almost half) during the nineteen-nineties, is attributable to the increase in teenage abortions due to abortion legalization. In contrast, the fall in general abortion rates accounts for a quarter of the decline. Hence, falling crime rates are largely attributable to abortion legalization resulting in better timing of births, rather than lower cohort size. Further, I find that the drop in teenage fertility rates during the nineteen-sixties and seventies, accounts for the entire fall in violent crime. This is probably in part, due to the increase in contraception sophistication (the pill) witnessed during that era.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".