Do future police officers want to pursue a crime scene examiner career? Exploring stability and change in police recruits’ interest in crime scene investigation
Bibliographic record
Abstract
Recruitment of high-calibre crime scene investigation candidates is a complex and important task for police managers. Genuine interest in a crime scene examiner (CSE) position might be a good indicator for recruitment strategies, as research has shown that good fits between vocational interest and work position can lead to better retention, motivation, commitment, job satisfaction and performance. Still, knowledge of the dynamics of interest in a CSE position among the pool of potential candidates remains scarce. Thus, this study uses a longitudinal research design to explore the evolution of 300 police recruits' interest in a CSE position throughout their police training curriculum. It shows that police recruits' interest in such a position is notably lower than their interest in other police roles. Findings also suggest that for many recruits, interest in a CSE position tends to decline over the course of their training, varying by gender, initial level of interest for the job, and the evolution of interest in other police functions. Accordingly, this research calls for further studies on police officers' aspirations to pursue a CSE career and advises caution in using genuine interests as a recruitment indicator, as a candidate's interest at a given moment may not reliably predict their long-term work orientation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".