Exploring Ethical Space as an Innovative Strategy to Foster Corporate Social Justice for Indigenous Students in Business Schools
Bibliographic record
Abstract
Improving outcomes for Indigenous students is a leading priority for Saskatchewan (Canada) educational institutions. Locally, at Edwards School of Business, there is lower than average retention rates for self-identified Indigenous students, a persistent trend in Canadian Business Schools. This research responds to the “engagement gap” identified by the Truth and Reconciliation Commission (TRC) calls to action 92 (ii). From this lens, we explored ethical space as an innovative approach enabling business schools to embrace sustainable practices to advance corporate social justice. Ethical space is required to fully transform partnerships into a mutually trusting, sustainable, and meaningful relationship required to improve retention and success for Indigenous students. We employed case study using semi-structured interviews to determine to what degree “ethical space” was present within Edwards School of Business. Overall, Indigenous students’ experiences were positive and it was evident that some degree of ethical space exists in classrooms. However, there is a lack of Indigenous content. Further, some students were reluctant to self-identify, fearing repercussions. Emanating from the findings, we recommend that more Indigenous content be added to the curriculum; that social events to encourage networking be increased and that a stronger connection within Indigenous communities be developed to foster business education.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".