Bibliographic record
Abstract
Introduction: Questions for a New Generation of Criminologists / Aaron Doyle and Dawn Moore Part 1: Canadian Criminology in the Twenty-First Century 1 The Dilemmas of Doing Criminology in Quebec: Curse or Opportunity? / Benoit Dupont 2 Reconciling Spectres: Promises of Criminology / Bryan R. Hogeveen 3 Commodifying Canadian Criminology: Applied Criminology Programs and the Future of the Discipline / Laura Huey Part 2: Expanding the Criminological Focus 4 Corporate and White-Collar Crime: Reflections on the Study of Financial Wrongdoing in the Era of Neo-Liberalism / James W. Williams 5 Criminological Nightmares: A Canadian Criminology of Genocide / Andrew Woolford 6 Power and Resistance in Community-Based Sentencing / Diana Young 7 Stigma and Marginality: Gender Experiences of Families of Male Prisoners in Canada / Stacey Hannem Part 3: Theory and Praxis 8 Reimagining a Feminist Criminology / Gillian Balfour 9 The Promise of Critical Realism: Toward a Post-Empiricist Criminology / George S. Rigakos and Jon Frauley 10 The Right to the City on Trial / Lisa Freeman 11 Anarcho-Abolition: A Challenge to Conservative and Liberal Criminology / Kevin Walby Index
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".