The Impact of Felony Diversion in San Francisco
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".