Editors’ note on special issue on indigenous knowledges, priorities and processes in qualitative research
Bibliographic record
Abstract
Editors' note on special issue on indigenous knowledges, priorities and processes in qualitative research Though scholarship on Indigenous organizations, practices and methodologies is rapidly growing alongside the burgeoning sub-discipline of Indigenous business and management, such research is not often reported in "mainstream journals." Rather, the research is commonly concentrated in Indigenous-or ethnic-focused journals. Recognizing the importance of these topics for all scholars, the editors of the journal of Qualitative Research in Organizations and Management invited us (the guest editors) to conceive of a special issue that would enable qualitative researchers and organizational management scholars to engage with the richness of Indigenous ways of knowing and the innovations resulting from methodologies that honour centuries-old knowledge and wisdom. As researchers of Indigenous organizations, management and policy, we called for a special issue that would bring Indigenous knowledges and methodologies to the broader discussion of qualitative methods in organizations and management.
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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 | 0.047 | 0.152 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".