Decolonizing STEM Learning through Land-Based Education in Ontario: The Generation of Guiding Principles and Discovery of Unanticipated Outcomes
Bibliographic record
Abstract
In Canada Indigenous learners are underrepresented in STEM-related subjects and fields. This is problematic since because Western science, by design, prides itself on being value-free, it struggles to address moral questions regarding how nature should be treated. Combining Indigenous knowledge with the tools of Western science, however, has the potential to enable Western science to be applied in a manner that helps to both generate and maintain reciprocal relationships with the natural world. This dissertation reflects on the process through which diverse stakeholders worked together to produce decolonized grades 7-10 STEM resources intended to engage Indigenous learners in STEM subjects and reconnect Indigenous and non-Indigenous learners to the land. Results shared consider the role that land-based learning and land education play in the decolonization of STEM education. The research study also reflects on the effects of producing decolonized STEM learning materials on the community and institutional stakeholders involved in the process. Findings indicate that the process of working together to produce decolonized STEM learning materials, in addition to the product itself, can spark both personal and institutional shifts towards decolonization.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".