A comparative study of prior learning for serving police officers in Canada and England and Wales, UK: Bridging the academic gap
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
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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