Bibliographic record
Abstract
Cet article décrit quelques systèmes communs d’information et des pratiques de la police à la lumière de la législation qui vise à protéger la vie privée des individus. L’auteur montre que le personnel policier est sensible aux droits de la personne et essaie de reduire les menaces posées par l’usage d’informations dont dispose la police. Les menaces sont de plus mitigées par l’absence d’information “soft ” dans le système national CIPC et les limites sur la capacité de partage électronique des données des centres locaux. Cepen-dant, l’auteur évoque certaines craintes au sujet de la qualité et de la sécurité des enregistrements, le niveau de qualification, les pratiques en matière d’information et les pressions en vue de relier les centres d’enre-gistrement locaux. Ces interrogations méritent l’attention des experts en matière de politique. This article describes some common police information systems and practices in light of legislation designed to protect individual privacy. The author finds that police personnel are sensitive to human rights issues and attempt to reduce threats posed by the use of police information. Threats are further mitigated by a lack of “soft ” information in the national Canadian Police Information Centre (CPIC) system and limitations on the ability to share electronically data in local agency records. However, the author also raises concerns about the quality and security of records, the level of training, questionable information practices, and pressures to link local records systems. These concerns merit more focused attention from policy experts.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".