Online Canadian police recruitment videos: do they focus on factors that potential employees consider when making career decisions?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given their potential to reach a large audience, online recruitment videos are likely a useful way for police services to recruit applicants. To increase the likelihood of people applying, these videos should focus on issues potential employees consider when making career decisions. A literature review revealed six job factors that people consider when contemplating a potential career. A coding framework focusing on these factors (and their respective sub-categories) was developed and applied to all available recruitment videos created by Canadian policing organizations (N = 37). The coding dictionary could be applied reliably and it revealed that only 23% of the job factors that emerged from the literature review are addressed in the videos and when they are, they are not particularly salient. Ways of using this study to develop more effective, data-driven, police recruitment videos are discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it