by Andrea Taylor-Butts Highlights
Bibliographic record
Abstract
• The role of private security in Canada is changing. According to the Census, in 2001, there were more people working in private security than there were police officers. Nationally, there were 10,465 private investigators and 73,535 security guards compared to 62,860 police officers. • From 1996 to 2001, the number of police officers per capita increased 2 % to 209 per 100,000 population but the total number of private security personnel per capita declined 2 % to 280 per 100,000. • While the number of security guards per capita remained stable, the number of private investigators declined 18%, lowering the overall rate of employment in private security as a whole. • Women represented about one quarter of private investigators and security guards and 17 % of police officers. The representation of women among each of the three occupational groups increased by three to four percentage points between 1996 and 2001. • Visible minorities constituted 13 % of the Canadian population (age 15 and over) in 2001 and represented 11 % of private investigators and 16 % of security guards. However, just 4 % of police officers were a visible minority. Since 1996, the representation of visible minorities among police officers grew 33%, while increasing 83 % among private investigators and 45 % among security guards. • Aboriginal persons made up 3 % of the overall population (age 15 and over), but constituted 4 % of police officers and were
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.121 | 0.070 |
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".