State Drug Sentencing Policy and its Impact on Public Opinion: Variations in Support for Police and the Criminal Courts by Race and Gender
Bibliographic record
Abstract
In the 1970s and 1980s the United States government initiated what we call the 'War on Drugs.' Soon after, state governments began to enact new legislation imposing mandatory minimum sentences for drug offenders, and eliminating judicial discretion in imposing sentences. It was not long, however, before the public began to react to the impacts of the sentencing laws and began to voice their opinions. After several years of sentencing under the new laws, many states, politicians, and judges became disenchanted with the harsh requirements and called for change. Since then some states have amended their drug laws to remove some of the previous mandatory sentences and restore judicial discretion. Some states, however, have declined to do so. Elected officials claimed the changes were made in response to public opinion which had grown tired of the harsh sentencing practices and the social and economic costs they imposed. Others have argued that the public has been supportive of tough criminal justice practices. In particular, Whites are often seen as supportive of strict sentencing practices which often disproportionately impact Blacks and Latinos. In this article I weigh in on this fractured debate, proposing an empirical test of whether or not states that continue to have strict drug sentencing laws have higher or lower levels of public support for local police and the criminal courts, and if the relationship varies by race or gender.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".