Bibliographic record
Abstract
Abstract In this paper, I examine the ethics of epistemic reparations in a decolonizing context. I argue there are underexplored and direct ways we must attend to the quality of our epistemic relations when engaging in epistemically reparative work. Good epistemic relations are a precondition on appropriately engaging in epistemic reparations in a decolonizing context, and potentially in a wider range of sites of epistemic exclusion. I develop a framework for thinking about epistemic relations, and highlight several advantages of the framework: in addition to opening important conceptual space for epistemic reparations in sites of epistemic exclusion, it sheds useful light on prominent existing work in the ethics of epistemic reparations, helps engage with underexplored skeptical worries about epistemic reparations in a decolonizing context, provides resources for responding to worries about privileging the perspective of the repairer, and helps clarify connections between interpersonal and structural aspects of the epistemic domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.139 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".