Reckoning with epistemological and ontological dissonance: a narrative inquiry into settler Canadian professors’ experiences decolonizing and Indigenizing the academy
Bibliographic record
Abstract
The Truth and Reconciliation Commission's (TRC) Calls to Action urge Canadian higher education (among other sectors of society) to learn the shameful truth about Indian Residential Schools and work towards reconciliation with Indigenous peoples. Through narrative inquiry, non-Indigenous academics shared their experiences of a faculty learning community on Indigenization of their teaching and curricular practices. They each, subsequently, co-composed with the researcher a narrative account based on those experiences. This paper presents one of the resonant threads that arose from the analysis of those narrative accounts, namely: Epistemological and Ontological Dissonance.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.054 | 0.080 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".