The Impact of Felony Diversion in San Francisco
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In the traditional criminal justice system, an arrest is followed by multiple decision points determining detention, prosecution, guilt, and sentence. Many jurisdictions across the U.S. are exploring alternative programs and approaches that consider individual needs and assessed risks at each decision point. San Francisco County, California, uses post‐filing pretrial diversion programs as alternatives to the traditional criminal justice system for defendants based on factors including social and behavioral needs. In this paper, we estimate the impact of a referral to felony pretrial diversion programs on case outcomes and subsequent criminal justice contact. To address selection bias associated with nonrandom assignment into diversion programs, we exploit the random assignment of felony cases to arraignment judges and use variation among judicial diversion referral rates as an instrument for the diversion referral. We find that a referral to diversion increases the time to disposition in the current case and decreases the probability of a subsequent conviction up to five years following case arraignment. Subgroup analyses find that the benefits of diversion are concentrated among females, those who are under the age of 25, and those facing drug sales charges.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 it