Bibliographic record
Abstract
Predictive policing is an emerging law enforcement technique that uses data and statistical analysis to aid in the identification of criminal activity. Its intention is to proactively reduce crime by providing police forces with likely areas of high risk variables. While this is a noble pursuit, every new tool must be accompanied by the ethical considerations of its potential consequences. Predictive policing is still in its infancy, borne from crime analysis and big data; however, the Western criminal justice system in the traditional sense is a reactive institution with a diverse history. The use of predictive policing presents a new challenge for law enforcement in that it allows for a divergence from the distinct reality of modern policing. Using the United States as an example of the dangers and flaws of predictive policing as a discretionary tool used to justify questionable processes and biases, this paper will analyze the potential opportunity that predictive policing and new holistic forms of law enforcement and community safety initiatives can use in partnerships with communities and policy makers.
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 | 0.163 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.086 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".