Bibliographic record
Abstract
Abstract This exploratory research investigated the retention challenges faced by Indigenous executives within Indigenous organizations, particularly focusing on First Nations band offices and nonprofit organizations in British Columbia. Drawing from the experiences of former and current Indigenous executives, as well as insights from current Indigenous organization leaders, the study sheds light on the dynamics of turnover in Indigenous workplaces. The results of interviews revealed that Indigenous executives in participating organizations averaged less than two and a half years tenure. They also suggested that the following factors affected the turnover of Indigenous executives: lateral violence, burnout, governance capacity issues, and inadequate compensation. Women with postsecondary education were the primary job incumbents in executive roles in Indigenous organizations, with the study identifying six distinct career trajectories among Indigenous executives. Additionally, the research highlights the detrimental impacts of executive turnover, including staff uncertainty and disruptions in business operations. Acknowledging its BC-specific focus and reliance on interviews with Indigenous executives, the study calls for future research to broaden its scope to encompass national Indigenous organizations and other provinces. It emphasizes the importance of fair compensation as a primary solution to address executive turnover, emphasizing competitive salary packages, benefits, and incentives. By implementing effective strategies, Indigenous organizations can attract and retain talented executives, thus mitigating turnover rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".