Understanding and Addressing Barriers to Indigenous Learners in Business and Accounting Studies
Bibliographic record
Abstract
This research addresses a significant gap in understanding the barriers Indigenous peoples face as they pursue business and accounting disciplines at the post-secondary level. Using structured interviews and content analysis, the study explored barriers and means the learners used and opined to remove or reduce those barriers. In addition, the research offers policy recommendations, including but not limited to the accounting profession, to address these challenges. The research also examines Indigenous peoples’ attitudes toward the accounting and business profession. This practical approach, with application to the accounting profession, bridges the practitioner-scholar gap noted in extant research. The findings suggest that the challenges are complex and interconnected and are deeply socialized in the fabric of Canada through societal biases and structural impediments within institutions that directly result from government policy, reserves, and residential schools. The research highlights issues not previously identified in the literature, including lateral violence and anti-business stigma within the Indigenous community as well as identifying an ontological reductionist fallacy of grouping dissimilar peoples together and forming policy as programs as though these groups were homogenous. Moreover, the colonial legacy, legislative and regulatory environments create a unique context for the study. It concludes that the nature of the problem may best be understood as a “wicked problem” and that collaborative approaches are likely the most suited to addressing the identified barriers. A conceptual co-evolutionary framework is proposed to relate the various barriers, structural impediments, and resulting barriers to business education specifically but also to post-secondary education generally.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.004 |
| 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".