An examination of the RCMP auxiliary program in British Columbia: through the lens of auxiliary constables
Bibliographic record
Abstract
This paper reports on the results of a survey of RCMP auxiliary constables in British\nColumbia. The purpose of the survey was to obtain a candid and instructive view of the work\nauxiliaries performed within the province and how RCMP auxiliary constables perceived their\nwork. The aim of the survey was to provide information to assist the RCMP in better\nunderstanding how the auxiliary program might be improved. The results of the survey suggested\nthat auxiliary constables were a diverse, well educated, and committed group of individuals who\ncontributed far more than is expected of them. The results also indicated that while there was a\nvery high level of satisfaction among auxiliaries for most aspects of the program, there were a\nfew issues that a significant number of auxiliaries expressed dissatisfaction with. These issues,\nnamely the auxiliary constable uniform and the extent to which they were respected and accepted\nby full-time officers, are the same issues identified in earlier studies of auxiliary constables.\nBased on the findings of the survey, this major paper offers suggestions on what the RCMP\nmight do to improve the auxiliary program.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".