Disrupting marginality through educational research: A wayfinding conversation to reorient normative power relations within Indigenous contexts
Bibliographic record
Abstract
This article explores Deborah Stone’s (1997) work regarding the types of language (specifically: symbols, numbers, causes, interests, and decisions) used to define and portray policy problems; and provides insightful discussion of the dynamics/complexity of such language within the context of an Indigenous educational initiative called the Pimacihowin Project. In becoming fluent in this type of language a researcher can learn to view educational policy problems from multiple perspectives. Stone’s work questions the ontological status of our own analytic concepts and reflects upon moral relativism. This analysis of insights gained from Stone’s work adopts a moral perspective, one grounded in social justice. Exploration of locally developed educational programs such as the Pimacihowin Project can provide great insight into culturally responsive pedagogy that is rethinking education, disrupting marginality, and meeting the needs of the communities that they serve.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".