Decolonizing Science: Undoing the Colonial and Racist Hegemony of Western Science
Bibliographic record
Abstract
Decolonization is the complicated and unsettling undoing of colonization. In a similarly simplified definition, science is a structured way of pursuing knowledge. To decolonize science thus means to undo the past and present racist and colonial hegemony of Western science over other, equally legitimate, ways of knowing. This paper discusses the paradigmatic prerequisites and consequences of decolonizing Western science. Only if Western science is toppled from its pedestal and understood in a cultural way can it engage with other sciences at eye level. Such equal collaboration that results in the co-creation of new knowledge based on the scientific method and Indigenous scientific inquiry is what decolonizing science is all about. What it looks like in practice is highly variable as there is no one-size-fits-all approach due to the fact that Indigenous knowledge is rooted in the local, the land. Therefore, decolonizing science is much more a path than a destination. This path, however, will also pave the way to a new multiparadigmatic space. A quick look into the history and philosophy of science reveals that new paradigms have always emerged after a few trailblazers started engaging in a new way of doing science.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.076 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".