A Canadian Perspective on Indigenous Peoples and Accounting Research: Using a Systematic Literature Review to Promote Inquiry and Inclusion*
Bibliographic record
Abstract
ABSTRACT This study uses a systematic literature review to explore academic research at the intersection of accounting and Indigenous peoples, focusing on the Canadian context. We systematically identified 15 English‐language, peer‐reviewed articles published in the review period of 1979–2019. We conducted detailed content and thematic analysis of the articles. Overall, we noted that research in this area is based upon archival documents or reviews of prior literature rather than qualitative or survey research methods. We found that much of the research has been driven by a small network of scholars, focusing primarily on themes of governmentality, imperialism, and accountability and control. In addition, research on Indigenous peoples and accounting in Canada has slowed over the past decade, which is inconsistent with global trends. Based on the systematic literature review, we offer specific, actionable recommendations to support inquiry and inclusion in the area of accounting and Indigenous peoples to move both research and society forward toward reconciliation. Our detailed recommendations aim to advance our understanding of the relationship between accounting and Indigenous peoples, foster an understanding of accountability issues relevant to Indigenous peoples in the Canadian accounting context, enhance support for Indigenous peoples studying business and accounting, and encourage Indigenous peoples to consider careers in accounting.
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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 | 0.139 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.045 | 0.041 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".