STEM Educational Outreach and Indigenous Culture: (Re)Centering for Design Scholarship
Bibliographic record
Abstract
Integrating Indigenous culture into STEM education is a critical process in building pathways to justice and diversifying design. This process serves to (re)center our conceptions of STEM education by challenging strictly Western notions of STEM, representing an opportunity for learning not just in curricular design, but in technological design as well. Postcolonial computing scholars have critically examined design processes, highlighting the dominance of Western knowledge undergirding cross-cultural design. However, such efforts have yet to fully leverage insights from national curricular (re)centering initiatives. We take up this opportunity through a qualitative case study of an educational outreach organization in British Columbia, Canada, a subsidiary of a nation-wide effort in curricular integration of Indigenous and Western STEM material. Applying postcolonial computing thought, we offer enrichments to theory by providing an empirical basis for a) integrating resiliency, b) politicization in design, and c) arguments for (re)centering epistemological authority in computing. These contributions both enrich theory and enhance the practice of cross-cultural design by encouraging and exploring an Indigenous (re)centering of our understanding of both curricular and technological design.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.066 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".