A comparative study of prior learning for serving police officers in Canada and England and Wales, UK: Bridging the academic gap
Bibliographic record
Abstract
The professionalisation of the police in Canada, and England and Wales has highlighted a gap in the education levels of new recruits and current serving police officers, motivating many of these officers to complete a university degree. The prior experience and training of these officers can be utilised as academic and operational credit against the learning outcomes of undergraduate programs and both countries use a system to recognise and dispense this award. In Canada this is called Prior Learning Assessment Recognition (PLAR) and in England and Wales, Accredited Prior Experiential Learning (APEL). The College of Policing also offers a system of Recognised Prior Learning (RPL) which tailors support to officers in accessing higher education programs. This paper examines how the two countries methods support the bridging of the academic gap between new recruit and long-serving officers, supporting the professionalisation transition of the police force to produce effective 21st century officers. Formalized partnerships between academic institutions and police services are rare, but the need for academic institutions to develop pathways for officers to complete higher level education is a positive step forward in the process. This review highlights how Canada has yet to engage with academia in the professionalisation process in the same way as England and Wales.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".