Bibliographic record
Abstract
This paper explores the integration of Traditional Ecological Knowledge (TEK) into secondary school curricula to enhance ecological literacy, cultural diversity, and sustainability education. TEK, rooted in Indigenous practices and place-based understanding, offers a relational approach to environmental stewardship, contrasting with the objective framework of Scientific Ecological Knowledge (SEK). Despite its potential, barriers such as insufficient teacher training and cultural sensitivity hinder implementation. This paper argues that TEK can foster critical thinking, strengthen Indigenous students' cultural identity, and promote cross-cultural awareness among non-Indigenous learners. By proposing hybrid pedagogical models combining TEK and SEK and drawing on Canada’s Truth and Reconciliation Commission’s Calls to Action (TRC, 2015), it advocates for experiential, community-based learning. While specific case studies on TEK integration are limited, this paper offers practical strategies for educators in Ontario and Canada to incorporate TEK, fostering sustainable practices and reconciliation.
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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 | 0.038 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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".