The Community-First Land-Centred Theoretical Framework: Bringing a ‘Good Mind’ to Indigenous Education Research?
Bibliographic record
Abstract
This article introduces an emergent research theoretical framework, the community-first Land-centred research framework. Carefully examining the literature within Indigenous educational research, we noted the limited approaches for engaging in culturally aligned and relevant research within Indigenous communities. The community-first Land-centred research framework was created by reflecting on how we engaged in research collaborations with Indigenous communities. This process of reflection led us to realize that within our research we had been developing a research framework that was culturally-aligned, relevant, and based on respectful relations that differed in important ways from other community oriented research framework. We articulate how we differentiate this framework from community-based approaches to research and discuss the community-first Land-centred research framework’s foundational principles. We draw upon lessons learned through our various collaborations over the past seven years. Key words: Indigenous; Land-centred research; community engagement
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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 | 0.082 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.170 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".