Accounting and First Nations: A Systematic Literature Review and Directions for Future Research
Bibliographic record
Abstract
Abstract This paper presents a synthesis of academic research focused on First Nations peoples, contrasting First Nations versus non‐Indigenous understandings of accounting and accountability. Key themes and trends in past research are identified across 51 publications spanning four decades, and directions for future research are proposed. The need for more culturally responsive accounting is well established, and past studies highlight the inadequacies of reporting practices which do not appear to capture the priorities and nuances of First Nations entities. The focus and execution of accounting research is shifting towards more contemporary experiences with accounting, and the contribution of First Nations worldviews to advances in non‐financial reporting. This paper systematically explains the inadequacies of contemporary reporting practices and encourages the accounting community to reflect on future opportunities. It is therefore relevant to both academics and practitioners seeking to uphold the rights of First Nations peoples to self‐determination in line with the United Nations Declaration on the Rights of Indigenous Peoples. Further work is urgently required to ensure First Nations organisations are adequately supported in their reporting practices, to incorporate traditional knowledges and to achieve positive outcomes for their communities.
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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 | 0.036 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".