Does nothing stop a bullet like a job? The effects of income on crime
Bibliographic record
Abstract
Do jobs and income-transfer programs affect crime?The answer depends on why one is asking the question, which shapes what one means by "crime."Many studies focus on understanding why overall crime rates vary across people, places, and time; since 80% of all crimes are property offenses, that's what this type of research typically explains.But if the goal is to understand what to do about the crime problem, the focus will instead be on serious violent crimes, which account for the majority of the social costs of crime.The best available evidence suggests that policies that reduce economic desperation reduce property crime (and hence overall crime rates) but have little systematic relationship to violent crime.The difference in impacts surely stems in large part from the fact that most violent crimes, including murder, are not crimes of profit but rather crimes of passion -including rage.Policies to alleviate material hardship, as important and useful as those are for improving people's lives and well-being, are not by themselves sufficient to also substantially alleviate the burden of crime on society.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".