The World Indigenous Research Alliance (WIRA): Mediating and mobilizing Indigenous Peoples’ educational knowledge and aspirations
Bibliographic record
Abstract
There is an Indigenous resurgence in education occurring globally. For more than a century Euro-western approaches have controlled the provision and quality of education to, and for Indigenous peoples. The World Indigenous Research Alliance (WIRA) established in 2012, is a grass-roots movement of Indigenous scholars passionate about making a difference for Indigenous peoples and their education. WIRA is a service-oriented endeavor designed by Indigenous scholars working in mainstream institutions to support each other and to provide culturally safe spaces to share ideas. This paper highlights how WIRA came to be, and outlines the nature and scope of these shared endeavours. Strategically, WIRA operates under the mandate of the World Indigenous Nations Higher Educational Consortium (WINHEC) who regularly report to the General Assembly of the United Nations Indigenous Peoples Permanent Forum on Indigenous Issues (UNPFII) pertaining to Indigenous Peoples and their education (United Nations Permanent Forum on Indigenous Issues, 2007). Indeed, this collaboration provides the opportunity to share best practices across respective countries, and to co-design interdisciplinary, dynamic and innovative educational research. Since the inception of WIRA, a number of research priorities have emerged alongside potential funding models we believe can assist our shared work moving forward. The launching of WIRA is timely, and sure to accelerate the goals envisaged by WINHEC, and Indigenous peoples aspirations in education more generally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".