16. Culture and Ethics in First Nations Educational Research
Bibliographic record
Abstract
In this paper, we share phenomena experienced by a multi-cultural research team working collaboratively with Wolastoq (Maliseet) First Nations Elders to document rapidly disappearing Wolastoq language, culture, and knowledge. This knowledge will ultimately be stored in databanks for future educational, community, and heritage use. Embedded within this research experience is a constantly evolving ebb and flow of culture, being, and relationships. As a collaborative research team, we explore ethical ramifications of dynamic, symbiotic relationships we share with Elder participants, requirements of university ethical review processes, and how this process shapes the knowledge that we collaboratively produce. We question how this nexus of cultures and ethics of researchers and collaborators directs the educational materials that we construct. Situated between the high tide of ethical standards and the low tide of the application of these ethics, is where the tides meet, and standards and praxis interact. Lastly, we suggest ways to supplement the ethics review process for social and educational research to better respect the individual rights and rationality of participants with whom we research, deepening the significance of such studies and promoting social justice.
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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 | 0.035 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.087 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".