Bibliographic record
Abstract
This article proposes the comparison of two methods of analysis, semiotics, and hermeneutics, to address contemporary issues in ethical and political philosophy, through the study of the phenomenon of epistemic injustice. Conceptualized by Fricker (2007), epistemic injustice is synonymous with the denial of the value of knowledge that an individual possesses because of prejudices about the social group to which he or she belongs or is affiliated. When epistemic injustice is studied in the empirical world, it poses some crucial issues in terms of interpreting the meaning that the individual gives to the experience of injustice that he or she experiences. Although the interpretation of injustice is central to the understanding of the phenomenon itself, little research in ethical and political philosophy addresses these aspects, because of the failure to sufficiently mobilize analytical methods such as semiotics and hermeneutics. However, these two methods, usually used in other fields to deal with these aspects, allow us to question the treatment and the interpretative scope of the epistemic injustice by the different interlocutors involved in the interaction in which it is reconducted. The comparison of these two methods in the analysis of epistemic injustice finally allows us to argue in favor of the hermeneutic method, as defined by Gadamer and rethought by Code (2003), to enhance Gadamer's legacy through the analysis of ethical and political issues in human sciences research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".