Decolonization of STEM in the Public Education System in Québec, Canada
Bibliographic record
Abstract
Indigenous representation in science, technology, engineering, and mathematics (STEM) is crucial for reconciliation, self-determination, and inclusive and equitable science policy. Indigenous people continue to be underrepresented in Canada's STEM workforce, creating a substantial annual cost to the Canadian economy. Canada’s provinces and territories hold jurisdiction over education, and the majority, including Québec, do not include Indigenous perspectives in their elementary and secondary STEM curricula. This exclusion can alienate Indigenous learners and deter them from STEM careers. As a model for the decolonization of STEM in other provinces, we call for the amendment of Québec’s Education Act to create an Indigenous Education Steering Committee (IESC), which would collaborate with the Minister of Education to ensure inclusion of locally relevant Indigenous STEM content in compulsory curricula. We further propose that Québec include continued professional development training for teachers on Indigenous perspectives in STEM in the Ministry of Education’s strategic plan, thereby building capacity for the equitable participation of Indigenous peoples in STEM.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".