What Happens When Investigating A Crime Takes Up Too Much Time? An Examination of How Optimal Law Enforcement Theory Impacts Sentencing
Bibliographic record
Abstract
Previous research finds that variations in sentencing outcomes still exist among similarly situated individuals, especially among drug offenders. While courtroom actors are often the focus of sentencing disparities, law enforcement officers are rarely studied. This is problematic because criminological research has yet to explore whether law enforcement could influence sentencing decisions. The current study aims to discover the influence of a previously ignored legal variable, investigation workload, on sentence length and directly examine an untested criminal justice theory, Optimal Law Enforcement Theory. This study will explore these overlooked concepts with a rare dataset that contains information on individuals convicted of prescription drug trafficking in Florida from 2011-2013. We find that investigation workload does influence sentencing, with offenders convicted from a high police workload being significantly more likely to experience longer sentences than offenders convicted from a low investigation workload. Limitations and policy implications are also discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".