Whose Knowledge Counts? Decolonial and Anticolonial Reckonings in STEM Education
Bibliographic record
Abstract
Globally, science, technology, engineering, and mathematics (STEM) education is widely framed as essential to innovation, economic growth, and social progress. These framings, however, often overlook STEM’s long-standing connections to colonialism, empire, and the exclusion of certain forms of knowledge. Decolonial and anticolonial perspectives are used to examine the assumptions about knowledge and reality that shape STEM education. While STEM education has played an important role in scientific and technological development, it has also reinforced hierarchies that marginalize Black, Indigenous, and other non-Western scientific traditions. These hierarchies persist through curriculum design, ideas about ability, language dominance, and universalist practices that treat Euro-Western science as neutral and authoritative. Bringing decoloniality and anticoloniality into conversation makes visible how STEM education has helped sustain exclusionary knowledge systems and colonial ways of knowing. Four recurring myths are identified that continue to structure STEM education and limit possibilities for meaningful change. Rethinking STEM education along these lines is necessary to support epistemic justice and to open space for more plural, relational, and equitable futures. Keywords: decoloniality; anticoloniality; STEM education; epistemic justice; coloniality
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.127 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".