From Nothing Works to What Works: Changing Professional Ideology in the 21st Century
Bibliographic record
Abstract
The authors explore changes over time in criminologists' “professional ideology”—a core set of underlying beliefs that focuses academic thinking along certain lines but not others. Until the late 1960s, criminologists believed that the scientific study of the causes of crime would form the basis of individualized treatments that would reduce offender recidivism. By the mid-1970s, this view had collapsed and had been replaced by a professional ideology emphasizing that “nothing works” in corrections, that the causes of criminality are structural, and that crime can only be reduced through social justice. Although not without its merits, the authors suggest that this professional ideology has had the unfortunate consequence of legitimating “knowledge destruction” (showing what does not work) as the core intellectual project of criminology and thus of undermining efforts at “knowledge construction” (showing what does work). A “what works” movement within corrections, however, is advancing an alternative professional ideology that, once again, endorses the use of science to solve crime-related problems. The authors believe that, if embraced, this vision will improve criminology as a discipline and contribute more than “nothing works” scholarship to the commonweal of both offenders and the public order.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.060 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.010 |
| 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".