Canadian researchers provide framework to encourage recruitment of Indigenous probation officers
Bibliographic record
Abstract
Purpose The purpose was to define different types of cultural experiences, events, activities and interventions that Indigenous people think will improve cultural diversity among probation officers in Canada Design/methodology/approach Based on interviews with eight Indigenous probation officers in British Columbia, the authors analyzed the results for thematic content, then proposed their framework. Findings After examining their results, the authors offered five principles to improve recruitment and retention. They were (1) developing competencies to recruit Indigenous people, (2) involving local managers and staff in recruiting, (3) providing support systems after being hired, (4) developing team and cultural values and norms, and (5) recognizing the tasks that Indigenous workers do because of their culture. Originality/value The underlying assumption of the research was to encourage cultural multiculturalism by focusing on experiences and events that improve diversity. The open-ended interviews allowed an in-depth exploration of viewpoints and practical solutions.
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.
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 | 0.063 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.040 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".