'Policing Rural Communities in North America': An International Society for the Study of Rural Crime Roundtable
Bibliographic record
Abstract
Rural crime and criminal justice practices and responses face different challenges from those experienced in urban contexts. A practitioner-focused roundtable, convened by The International Society for the Study of Rural Crime (www.issrc.net), investigated challenges and innovations in international contexts on issues surrounding rural policing with a specific focus on rural policing in Canada and the United States. The roundtable was held online on 15 September 2021 and was moderated by Dr. Jessica Peterson, formerly of the University of Nebraska at Kearney (now an Assistant Professor at Southern Oregon University). Panellists were asked to respond to three key questions: What is the key element to successful community policing in your community? What is one initiative in which you have successfully engaged the community in crime-reduction efforts? What is the most significant challenge to successfully reducing crime in your community? The following are transcripts of the four presentations from the panelists on this Roundtable.
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 | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".