Developing Gadamerian Virtues Against Epistemic Injustice: The Epistemic and Hermeneutic Dimensions of Ethics
Bibliographic record
Abstract
In her groundbreaking text Epistemic Injustice, Miranda Fricker evaluates types of harms incurred by individuals undergoing unrecognized and inarticulable oppression. At issue in epistemic and hermeneutic injustice are prejudicial comportments to and evaluations of reality. In the following, I focus on hermeneutic and epistemic injustice in relation to the formation of intellectual and ethical virtues. When reading Fricker and Hans-Georg Gadamer’s hermeneutics together, there is a clear pathway to improve ethical development. In particular, ethical development ought to cultivate the proper virtues that promote understanding. Gadamer’s emphasis on the qualities of a researcher and the epistemic virtues that Fricker highlights reveal an educative path for addressing injustice. In other words, cultivating these virtues counteracts injustice wherein recognition and articulation of reality is challenged or at issue.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".