The Legacy of D. A. Andrews in the Field of Criminal Justice: How Theory and Research Can Change Policy and Practice
Bibliographic record
Abstract
Donald Andrews’ overarching goal in the field of criminal justice was to assist offenders in changing their antisocial behavior. His means of doing so was to assist criminal justice agencies and their practitioners by changing the manner by which they worked with offenders. Beginning with the principles of differential association and social learning theory, Andrews crafted his own version of the psychology of criminal conduct (PCC). As a result of PCC, terms like “criminogenic needs” and “risk-need-responsivity” (RNR), which he coined and then researched extensively, have become commonplace in the lexicon of corrections. But that is only the beginning of his legacy. Translating theory to practice and then implementing it in countless criminal justice agencies represents his second monumental contribution to correctional and forensic psychology. The tools he developed were vital to his linking assessment with intervention and his translating theory and research to policy and practice. He also insisted that these efforts be conducted in a humane and just manner.
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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 | 0.077 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.081 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".