Epistemic Injustice and Recognition Theory: A New Conversation —Afterword
Bibliographic record
Abstract
The notion of recognition is an ethically potent resource for understanding human relational needs; and its negative counterpart, misrecognition, an equally potent resource for critique. Axel Honneth’s rich account focuses our attention on recognition’s role in securing basic self-confidence, moral self-respect, and self-esteem. With these loci of recognition in place, we are enabled to raise the intriguing question whether each of these may be extended to apply specifically to the epistemic dimension of our agency and selfhood. Might we talk intelligibly—while staying in tune with Honneth’s concepts and their Hegelian key—of a generic idea of epistemic recognition? Such an idea might itself be seen to apply at the same three levels to indicate: first, basic epistemic self-confidence; second, our status as epistemically responsible; and third, a certain epistemic self-esteem that reflects the epistemic esteem we receive from others. The papers in this volume surely sound a chord in the affirmative, and together they steer us towards a multifaceted conception of how epistemic injustice is related to epistemic misrecognition, and indeed how we might construe a positive relation of epistemic recognition.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".