Performing Survivance: (Re)Storying STEM Education from an Indigenous Perspective
Bibliographic record
Abstract
Abstract There is a growing realization from academics, the public, and Indigenous Peoples for compelling new narratives to reshape the ‘progress narrative’ of modernity based on the classical scientific paradigm that has privileged mind over body, heart and spirit; human over more-than-human; and overlooked the worldviews and knowledges of Indigenous Peoples. The prevailing narrative has created an imbalance that impacts the ethnosphere and the biosphere. Regardless, mainstream education is uncritically promoting STEM (science-technology-engineering-mathematics) thinking, an agent of empire fueling the state-military-industrial-education complex, This paper is a call to widen the Eurocentric and anthropocentric knowledge base of mainstream education to include as ‘equivalent’ Indigenous and other Other(ed) worldviews and epistemologies. This (re)storying of STEM is based on the teachings of my Elders and recent research with my community in British Columbia in solidarity with Indigenous communities in Peru as we work to regenerate more complex, culturally-inclusive possibilities for living together on a shared planet.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| 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".