The Moral Epistemology of First Nations Stories
Bibliographic record
Abstract
One way to view the importance of storytelling in First Nations cultures is to look at the epistemology that informs storytelling and, more generally, practice in those cultures. Listening to the First Nations voices of Carol Geddes on respect, Louise Profeit-Leblanc on responsible truth, Vine Deloria on principles of epistemological method, and Deloria and Lee Hester on the centrality of belief in the West in contrast to the centrality of practice, experience, and story in indigenous worlds suggests that storytelling should be central to environmental education, ethics, and practice. Résumé Une façon de considérer l’importance de l’art de raconter dans les cultures des Premières Nations consiste à examiner l’épistémologie qui informe cet art et, plus généralement, la pratique dans ces cultures. L’écoute des voix des Premières Nations, comme celle de Carol Geddes sur le respect, de Louise Profeit-Leblanc sur la vérité responsable, de Vine Deloria sur les principes de la méthode épistémologique et de Deloria et Lee Hester sur la centralité de la croyance dans l’Occident par contraste avec la centralité de la pratique, de l’expérience et du récit dans les mondes autochtones, suggère que l’art de raconter devrait se situer au centre de l’éducation environ-nementale, de l’éthique et de la pratique. I I teach environmental ethics and First Nations philosophy. I came to think
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.061 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".