How are organizations supporting Indigenous employees? Organizational Leaders rankings of potential inducements for Indigenous staff in a post-secondary institution
Bibliographic record
Abstract
In response to the Truth and Reconciliation Commission of Canada’s Calls to Action, Canadian universities are increasingly engaging in Indigenization efforts, often through the recruitment of Indigenous faculty and staff. However, meaningful institutional support for these individuals remains inconsistent and underdeveloped. Drawing on the Employee–Organization Relationship framework and Multilevel Job Demands–Resources Theory, this study explores the organizational determinants that influence the implementation of supports for Indigenous employees at a large Canadian post-secondary institution. This study used a concurrent mixed methods design, integrating a card-sorting technique into in-depth interviews to explore organizational leaders’ perspectives on the implementation and feasibility of supports for Indigenous employees. Reflexive thematic analysis was used to identify key themes from the qualitative data, while exploratory descriptive and inferential statistics were applied to analyze the sorting data. The overall perceived feasibility of supports was low, suggesting that many were not seen as easy to implement. Thematic analysis identified four key themes as determinants of implementation: The Leadership Axis, Legacies in the Walls: Structural & Systemic Factors, The Currency of Implementation, and Ignorance. The findings highlight that while universities benefit from the contributions of Indigenous employees, the feasibility of providing reciprocal supports is shaped by a range of barriers and facilitators, both within and beyond the employee–organization relationship.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".