Recruitment and retention challenges of a regional centre of a resource-based region: The case study of Prince George, BC.
Bibliographic record
Abstract
This thesis challenges the idea that only small resource-based communities have recruitment and retention issues by examining a regional centre of a resource-based region, Prince George, British Columbia. To test whether the pull and push factors were consistent between these two types of communities, this thesis surveyed new residents to Prince George and human resource professionals. The results confirmed that Prince George shares many of the same pull factors (e.g., employment, natural setting, and friendliness) and push factors (e.g., loss of employment, climate, and isolation) of smaller resource-based communities. The number of services did not appear to be as large an issue for Prince George as it was for the smaller communities. For example, the presence of post-secondary institutions was viewed very positively. Prince George's most important push factor, poor air quality, appears to be unique to this case study as the literature does not identify environmental problems as a general push factor of resource-based communities. It is important to note that Prince George's recruitment and retention issues were not perceived to be as severe as those facing many of its smaller neighbours. Recruitment and retention are important governance issues as the inability to attract professionals can severely hamper overall community development and many of the push and pull factors represent broader quality of life issues. As such, recruitment and retention issues are not just a business issue. Therefore, addressing the push and pull factors will require the collective action of multiple actors.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".