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Record W3204447620 · doi:10.38126/jspg180402

Decolonization of STEM in the Public Education System in Québec, Canada

2021· article· en· W3204447620 on OpenAlexaffabout
Emma Anderson, Kaitlyn Easson, Saina Beitari, Maïa Dakessian, Sai Priya Anand, Sumedha Sachar, Jessica Bou Nassar

Bibliographic record

VenueJournal of Science Policy & Governance · 2021
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsIndigenousCurriculumInclusion (mineral)WorkforcePolitical scienceJurisdictionPublic administrationEconomic growthSociologyLawSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.326
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes2
Has abstractyes

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