Somebody's watching me: examining the impact of probation officer caseloads on revocation rates
Bibliographic record
Abstract
There are approximately one quarter of a million individuals on supervised probation in California.This is more than the number of people incarcerated in, or on parole from, state prisons, and equates to roughly one in every hundred California adults.As the most substantial means of correctional supervision in the state, probation is a crucial piece of public safety when policymakers consider potential changes to any statewide approach to criminal justice.Prior research into probation as a system indicated several critical factors for predicting the likelihood of a probationer's success or failure: education, criminal background, economic and family ties, race and ethnicity, and mental health.These important elements of a probationer's life are significant predictors of whether the probationer will complete the term of his or her supervision.However, these are systemic realities that are often hard to solve, or even clearly identify, through targeted policy decisions.However, there are other factors entirely within reach of policy intervention.One example is the use of standardized or well-defined and appropriate caseloads for probation officers.Historically, researchers have explored the impact of probation officer caseload sizes on outcomes and found mixed results, mostly because of the unique circumstances of each study. My research utilizes a regression analysis of probation revocations inCalifornia's 58 counties over eight years between 2010 and 2017.The primary focus of the regression is a vi comparison of the revocation rate and the overall caseload size in each county, although I also examined other factors such as county racial demographics, education attainment, and economic metrics.Additionally, I provide additional context and insight into the implications suggested by the regression results and potential policy avenues to improve probation in California.I found in my regression results that population density, the county's median age, the ratio of probationers to probation officers, and the level of state funding provided through the California Community Corrections Performance Incentive (SB 678) program all impact a county's probation failure rate at statistically significant levels.Comparing against existing literature and noting the limitations of this particular study, I find that careful management of differentiated caseloads based on an offender's potential risk to re-offend and increasing financial incentives to counties are beneficial policy actions to reducing the likelihood of probationers failing the terms of their supervision.
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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 | 0.006 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".