Bibliographic record
Abstract
American crime prevention is at a crossroads. After decades of successful efforts, the concept of prevention has been embedded in the American lexicon and prevention strategies are becoming a part of public policy. Even so, there is great uncertainty about the form, function, and emphasis of prevention programs. Historically, prevention efforts have used techniques of surveillance and incapacitation and focused primarily on guns, gangs, and drugs. Over the past 10 years, more progressive forms of prevention have been incorporated into public policy. However, the current conservative climate, combined with the fear of terrorism and declining sources of revenue, has precipitated a renewed emphasis on surveillance and incapacitation. Because the United States does not have a specific agency responsible for crime prevention, or even a national crime prevention agenda, much American crime prevention is incident driven. At present, the themes of information integration systems and prevention technology, law enforcement partnerships, and targeted interventions dominate the discourse on American crime prevention. However, it is unlikely that current crime trends will continue. Despite the impressive body of evidence accumulated over the last several decades on the importance of evidence-based crime prevention efforts, there is still enormous pressure to go back to the old-fashioned logic of deterrence and punishment.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".