Bibliographic record
Abstract
I am deeply to the many people who have made this book possible.Thank you first of all to the dozens of Halifax doorstaff, Halifax police officers, and rcmp members who participated in this study.I am grateful to you for opening up your minds to the research process.Your candid commentary about nightclubs is now the core of my analysis, and I hope I have done your thoughts justice.Thanks especially to "Monster" Joe Baldwin (now retired) for orienting me to the Halifax nightclub scene.Much of the ethnographic and interview data that appears in this book was collected by Andrew Dunn and David MacDonald, both former students at Saint Mary's University, who despite telling me that getting paid to hang out in nightclubs was the best gig they could have ever hoped for, have wisely moved on to greener pastures.Two other research assistants, Neera Datta and Jillian Cameron, also conducted interviews.Their amiable natures made it easy for research subjects to speak freely, and this is repeatedly evident in the data.Thank you also to Sandi Cole-Pay and Lindia Smith for transcribing sometimes muffled and indecipherable interview data with diligence and care.Stephen Perrott of Mount Saint Vincent University gave me access to his invaluable expert knowledge on measuring police culture and in-group solidarity as well as practical
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.291 | 0.191 |
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".