Public Criminology in an Age of Austerity: Reflections from the Margins of Drug Policy Research
Bibliographic record
Abstract
On May 14th 2001 the author was invited to testify in Ottawa as an ‘expert witness’ by the Senate Committee on Illegal Drugs. Based on this experience, the present paper offers insight on the matter of presenting research with the aim of influencing drug policy discussions. The testimony was derived from statistics produced with standard survey items and measures for studying patterns and problems of cannabis use. Among other observations, the interpretation given was that, even at high use levels, marijuana users experience few symptoms of dependence or abuse. Excerpts from this testimony, and other work submitted, are cited in the 2002 report of the Committee. The potential impact of the testimony given is examined in this paper in relation to the other submissions which were based on qualitative research and one that was explicitly polemical in nature, or derived from ideological assertions by the author. The excerpts from these works that were eventually included in the final Senate report suggest that scientific arguments per se were not deemed more persuasive in this forum than the use of other kinds of rhetoric or evidence. These observations will be further situated in the context of scholarly discussion about the challenges and prospects of Public Criminology and the role of academics as “democratic underlabourers.”
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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 | 0.114 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.073 | 0.137 |
| Scholarly communication | 0.051 | 0.032 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.032 | 0.055 |
| Insufficient payload (model declined to judge) | 0.004 | 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".