The Absentee Formal Education in Prison Guard Hiring Traditions: Extrapolating Pareto Distance to Inform Personnel Optimality for Corrections Agencies
Bibliographic record
Abstract
The educated corrections officer/guard is sufficiently schooled in the relevant social science area and possesses sufficient theoretical knowledge such that the quality of work, purpose, and goals of incarceration could be met. Thus, the desire to bring professionalism into the field of corrections has been recognized for many decades, particularly after the Attica tragedy of 1971. However, in pursuit of adequate staffing levels many factors (geography, for example) diminish the ability of prisons and correctional facilities to obtain formally educated employees. This mixed-methods research aimed to first identify prison policies through random selection of state corrections agencies in the United States (n=20) that may allow certain years of service as a substitute for a bachelor’s degree in social sciences at hire. Secondly, there was a need to define how to calculate Pareto Distance (PD) as an indicator of incongruous education standards as to prison guards, and third, substantiate recommendations for benchmark employment to at least 1-in-5 guards with a baccalaureate. Unfortunately, the results were compelling. The majority of states permit teenagers to apply to work as prison guards. The incarceration rate is closely tied to the education level throughout the state. The Pareto Distance, however, represents a prospective benchmark for optimality where insufficient numbers of educated personnel are available to effectively operate a prison.
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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 | 0.010 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".