Police recruitment videos and their relevance for attracting officers
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
Abstract Police continue to cite struggles of attracting applicants to their agencies. One means by which police attempt to attract applicants is via their recruitment videos. As part of the present research, I employ content analysis to descriptively assess the material contained within a large sample of recruitment videos from police agencies across the USA (N = 567). Trained coders reviewed each video and coded them for an array of different variables, including video characteristics, officer representation, informational content, and behavioural content. The analyses reveal that in addition to including some technical information about the job, many videos also feature high-speed driving, the use of firearms, the demonstration of canine as well as special weapons and tactics units, and an emphasis on men, masculinity, and physicality. Although many videos still highlight some community-oriented behaviours, such behaviours are often less salient than the former. By cataloging recruitment videos, I both identify and interrogate the behaviours highlighted by police as part of their recruiting efforts and discuss the associated implications for people’s potential interest in policing careers.
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.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it