"Start with where you are": The View of Indigenizing STEM Curriculum from Educational Outreach
Bibliographic record
Abstract
As educational institutions in Canada respond to the Truth and Reconciliation Commission's 2015 "Calls-to-Action" by exploring what it means to "indigenize" curriculum, the process is complex and requires contributions from multiple angles of education, including informal education. This is particularly important for STEM education, where the exclusivity of western centric notions of science and technology must be re-evaluated to provide a more culturallyaware offering. The unique position of informal education programs like educational outreach provides a unique outlook that offers lessons that formal education can benefit from. To explore this unique position in indigenizing, we use a qualitative study with Geering Up, a STEM educational outreach program at the University of British Columbia, and members of K'omoks First Nation on nearby Vancouver Island. We conducted semi-structured interviews with 10 members of Geering Up and 4 members of K'omoks First Nation, and identified themes that ought to inform how educators and scholars consider the foundations of indigenizing curriculum and education in general, particularly the value of sharing. We explore its potential as the foundation of a broad framework for indigenizing curriculum in a way that scales from one community's perspective to multiple in a way that is respectful, and accounts for the significant time, energy, and human resource commitment involved in these practices.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.047 | 0.105 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.012 |
| 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".