In defence of materialism: a critique of Afrocentric ontology
Bibliographic record
Abstract
Over the last twenty-five years, Afrocentric thinkers have made notable contributions to the ongoing task of challenging Eurocentrism. In the course of so doing, however, some measure of over-reach has occurred. In particular, Afrocentrists have coupled their general critiques of Eurocentrism with specific rejections of its putatively constituent elements, one of which is materialism. Among other things, this rejection of materialism has led Afrocentrists to refrain from interrogating capitalism, downplay the structural dimensions of racial oppression and elevate the ontological status of culture to the point where it is posited as the dominant source of pressing problems affecting people of African descent in the US. Such an outlook undermines the potential of Afrocentricity to be a force for radical social change.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.077 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| 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".