Defining, Designing And Deploying Indigenous Research Methodologies In Management Research
Bibliographic record
Abstract
Over the past two decades, there has been substantial growth in Indigenous-focused research within the field of Management and Organization Studies (MOS). This symposium aims to address the imperative for rigorous and community-connected MOS research with Indigenous Peoples. The symposium highlights the shift from viewing Indigenous Peoples merely as subjects of research to involving them as research partners. Emphasizing ethical considerations and acknowledging historical disenfranchisement, this session underscores the increasing demand from Indigenous communities for researchers to demonstrate competency in Indigenous Research Methodologies. This symposium invites Academy of Management members to explore and learn from emerging experts about designing and deploying Indigenous Research Methodologies in management research. Organizers will provide an overview of the state of Indigenous Research Methodologies in the MOS field, introduce their own methodologies, and engage in a moderated panel discussion.
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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 | 0.278 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".