Bibliographic record
Abstract
The concept of epistemic freedom, which I introduced in Epistemic Freedom in Africa: Deprovincialization and Decolonization (2018), is elaborated in this article to give it context and to highlights its itineraries. This reflective article provides five problematic epistemes that necessitates struggles for epistemic freedom. These are racist, (en)slave, colonial/imperial, endocentric/patriarchal, and capitalist/neoliberal epistemes. These inextricably intertwined epistemes are constitutive of Eurocentric epistemologies and its reproductions at a world scale and make it difficult for alternative epistemologies from the Global South to flourish. They also underpin contemporary global economy and its asymmetrical power dynamics, which continues to marginalise decolonial ways of thinking, seeing, and praxes.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.126 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".