Chain of Command: Information Sharing, Law Enforcement and Community Participation
Bibliographic record
Abstract
Information sharing among law enforcement officers and between law enforcement officers and the public is crucial to creating safe neighborhoods and developing trust between members of society. Since the terrorist attacks on the US in 2001 the US government has implemented a program called the information sharing environment: for both national security agencies and local law enforcement communication and sharing information is a top priority. Human information behavior and human information interaction research has been conducted in a variety of environments yet there is little research related to law enforcement and the public. This note presents early case study research in to this complex information sharing environment. The work builds on the strong tradition of research in information science related to information behavior and hopes to bridge the gap between security and law enforcement conceptions of information sharing and that of information science. This research is being conducted with the collaboration of a major metropolitan police department in the southern United States. The diverse research team brings together an academic, a law enforcement consultant and a constable from Toronto, Canada. While one deliverable of the project is to provide the law enforcement agency with a strategic communication and social media plan; the larger goal is to begin a multiple case research project to develop our understanding of information sharing with these types of unique stakeholders and in these complex environments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".