“Highlights” of a Criminological Career: Anthony Doob and the State of Evaluation Research in Canada
Bibliographic record
Abstract
Anthony Doob's career as a criminologist has been distinguished by his commitment to evidence-based policy development and to making criminological research relevant not just to other scholars but, perhaps more importantly, to policy makers, criminal justice practitioners, and the public. This paper provides an overview of the underdeveloped state of evaluation research on crime prevention and policy in Canada and discusses some of the obstacles to conducting rigorous evaluations. Fortunately, these obstacles – which include a tendency to rely on ideology and intuition as opposed to empirical evidence and a lack of sufficient resources for evaluation studies – have not discouraged Doob in his efforts to improve our criminal laws and justice system so that they reflect the fundamental Canadian values of justice, fairness, and humanity. Through his own research and his promotion of others' research – through, for example, Criminological Highlights – he has worked tirelessly to promote interventions by the criminal justice system that are informed by these values as well as by sound empirical evidence as to their effectiveness.
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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 | 0.049 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.012 |
| 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".