A History of Police and Masculinities, 1700-2010
Bibliographic record
Abstract
Introduction, David G. Barrie and Susan Broomhall 1. The Paternal Government of Men: The Self-Image and Action of the Paris Police in the Eighteenth Century? David Garrioch 2. 'A Species of Civil Soldier': Masculinity, Policing and Military in 1780s England, Matthew McCormack 3. Making Men: Media, Magistrates and the Representation of Masculinity in Scottish Police Courts, 1800-1835, Susan Broomhall and David G. Barrie 4. Becoming Policemen in Nineteenth-Century Italy: Police Gender Culture Through the Lens of Professional Manuals, Simona Mori 5. Men on a Mission: Masculinity, Violence and the Self-Presentation of Policemen in England c.1870-1914, Francis Dodsworth 6. Shedding the Uniform and Acquiring a New Masculine Image: The Case of the Late Victorian and Edwardian English Police Detective, Haia Shpayer-Makov 7. 'Well-set up men': Respectable Masculinity and Police Organizational Culture in Melbourne 1853-c.1920, Dean Wilson 8. Of Tabloids and Gentlemen: How Depictions of Policing helped Define American Masculinities at the Turn of the Twentieth Century, Guy Reel 9. Quiet and Determined Servants and Guardians: Creating Ideal English Police Officers, 1900-1945, Joanne Klein 10. Science and Surveillance: Masculinity and the New York State Police, 1945-1980 Gerda W. Ray 11. Managerial Masculinity: An Insight into the Twenty-First-Century Police Leader, Marisa Silvestri
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".