“So what does all of this have to do with Criminology?”: Surviving the Restructuring of the Discipline in the Twenty-First Century
Bibliographic record
Abstract
This commentary reflects back on an article that we published in the 1999 volume of this journal, which offered a number of observations about the condition of Canadian academic criminology at the turn of the new century. In this brief update, we consider some of the trends that have unfolded over the intervening six years, which have contributed to the continuing polarization of the discipline and the resurgence of traditional paradigms of state crime control and order maintenance (albeit under the purportedly new banners of risk management, computational criminology, administrative criminology, crime mapping, and the like). While innovative, progressive, and counter-hegemonic work continues to flourish in many quarters, the reward structures of twenty-first-century corporate university systems and criminological research environments militate overwhelmingly, and increasingly, in favour of the (re)ascendant “new orthodoxy.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.073 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.014 | 0.015 |
| 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".