Bibliographic record
Abstract
The aim of this paper is to show that, for the purposes of addressing the epistemic aspects of systemic injustice, we need a notion of emancipatory attention. When the epistemic and ethical elements of an injustice are intertwined, it is a misleading idealisation to think of these epistemological elements as calling for the promotion of knowledge through a rational dialectic. Taking them to instead call for a campaign of consciousness-raising runs into difficulties of its own, when negotiating the twin risks of being presumptuous about one’s own ignorance, and patronising in attributing ignorance to others. To arrive at a better response, we should follow Marilyn Frye’s suggestion that the epistemic aspects of injustice are, at root, problems of attention. But we fail to give an adequate account of this if we adhere to the most influential tradition of thinking about attention’s ethics, which takes its lead from Iris Murdoch’s reading of Simone Weil. That tradition addresses attention’s significance in individual contexts, rather than social ones. To get a better conception of the role that is played by attention in projects of social emancipation, we should take some ideas from recent work on the metaphysics of attention, together with ideas from an older tradition – represented here by R.G. Collingwood’s The Principles of Art – concerning the forms of attention that are occasioned by the creation and appreciation of art.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".