“Prediscursive Epistemic Injury”: Recognizing Another Form of Epistemic Injustice?
Bibliographic record
Abstract
This article revisits Miranda Fricker’s Epistemic Injustice (2007) through one specific aspect of Axel Honneth’s recognition theory. Taking a first cue from Honneth’s critique of the limitations of the “language-theoretic framework” in Habermas’ discourse ethics, it floats the idea that the two categories of Fricker’s groundbreaking analysis—testimonial and hermeneutical injustice—likewise lean towards a speech-based metric (equating harm to the capacities to know with compromise to the capacity to speak of what one knows). If we accept, however, that there are also implicit, preverbal, affective, and embodied ways of knowing and channels of knowledge transmission, this warrants an expansion of Fricker’s original concept. By drawing on Honneth’s recognition theory (particularly his Winnicottian-inspired account of ‘first order’ recognition and basic trust), I argue it is possible to extend the account of epistemic injustice beyond Fricker’s two central categories, to glimpse yet another register of serious “wrong done to someone specifically in their capacity as a knower.” I define this harm as prediscursive epistemic injury and offer two central cases to illustrate this additional form of epistemic injustice.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 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".