Places of Knowing, Places of Learning: Indigenous Place-Based Education in Canada
Bibliographic record
Abstract
This thesis reviews the literature on indigenous place-based environmental education in Canada. The concept of place is considered a starting point to localize, decolonize and integrate indigenous and non-indigenous knowledges (the culturally-situated subjective and intersubjective ways of knowing and meaning-making) in mainstream environmental education. Following a discussion of how a critical pedagogy of place can be situated in indigenous contexts, this thesis explores how indigenous and non-indigenous peoples and their knowledges can contribute to a place-based environmental education. While mainstream environmental education is conventionally considered the domain of Western sciences, knowledges of all cultural groups are needed to address the environmental challenges of the 21st century and enrich sustainability education. The inclusion of indigenous and other knowledges in mainstream curricula can foster intercultural understanding between indigenous and non-indigenous peoples. This can help to heal the relationship between indigenous and non-indigenous peoples in Canada after centuries of colonialism, assimilation, and discrimination against indigenous peoples. Transdisciplinarity and social learning theory can provide epistemological and methodological frameworks for the integration of indigenous and other knowledges in mainstream environmental education for an inclusive, place-based education.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".