Bibliographic record
Abstract
Introduction In the ongoing story of police reform – which in the Anglo-American world has largely been the story of efforts to make policing both more effective and more ‘democratic’ – the ideal of professionalism plays an ambiguous role. On the one hand, there is a long tradition of calls for the police to be more ‘professional’. In the United States, in particular, there was a period in the mid-twentieth century when virtually every effort at police reform marched under the banner of police professionalism (President’s Commission, 1967: 20-21; Carte and Carte, 1975: 114-115; Sklansky, 2008: 35-37; Segal, 2001) and echoes of that period can be heard today in arguments for a ‘new professionalism’ in law enforcement (Stone and Travis, 2011). In the United Kingdom, where police reformers often hearken back to Sir Robert Peel, the term ‘professional’ is sometimes used to sum up what was distinctive about the style of law enforcement that Peel pioneered, and – to take a particularly important present-day example – Peter Neyroud’s recent review of police leadership and training places heavy emphasis on the importance of developing ‘a new and vibrant professionalism in policing’ (Neyroud, 2011: 14). Nor are Britain and the United States unique in this regard. The ideal of police professionalism has long attracted reformers throughout the English-speaking world, and it continues to do so (Clarke, 2005: 642; Canadian Association of Chiefs of Police, 2012). For example, calls for police professionalism are heard loudly today in South Africa, where it is seen as a critical component of efforts to reduce corruption among law enforcement officers and to tame the use of deadly force by the police (Bruce, 2011: 6-8; Newham and Faull, 2011: 46-47, 51, 53).
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".