Bibliographic record
Abstract
Throughout the early part of the 2010s, the City of New York, and New York Police Department (NYPD) in particular, were generating significant positive attention over the fact that the city’s crime rate had dropped at about twice the national rate (Roeder et al, 2015). Heralded as the ‘New York Miracle’, commentators observed how the City that had formerly stood as a symbol for crime, social disorder, and urban decay was now the City that had become ‘safe’ (Zimring, 2013). Some have credited NYPD’s use of Wilson and Kelling’s (1982) ‘broken windows’ approach as generating declines in certain offences (Sengupta and Jantzen, 2018); others cite police use of the CompStat model to target district-level offences as the primary catalyst of change (Zimring, 2013). Regardless of what actually drove New York’s crime decline, cities across the globe began to look to New York as offering potential solutions to adopt in response to their own local conditions. That New York became a site from which other, frequently smaller, police services sought to poach crime fighting ideas is not surprising in one sense: larger police services often have the financial and human resources to develop and test their own innovations and/or to trial new technologies. To illustrate: the first major trial of body-worn cameras (BWCs) in the UK was with Devon and Cornwall Police, and the Toronto Police Service were the site of the first trial in Canada, both agencies with more than a couple of thousand sworn officers. Similarly, we find two other major municipal agencies – Los Angeles and Dallas – as two early adopters of predictive policing software.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.270 | 0.101 |
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".