Stop Talking and Listen: Discourse Ethics and Feminist Care Ethics in International Political Theory
Bibliographic record
Abstract
This article seeks to extend feminist critiques of Habermasian discourse ethics in International Relations by engaging with the feminist ethics of care. Using the work of Andrew Linklater as a starting point, it argues that neither the existing critiques nor the responses have adequately addressed the key features of care ethics. The article critiques the idea of ethics as dialogue among ‘human beings as equals’ through an elaboration of several features of the ethics of care: firstly, the importance in care ethics of ‘dependency’ and ‘vulnerability’ not as conditions to be overcome, but rather as ways of being for normal human subjects; secondly, the focus on the responsibilities for listening attentively to the voices of others rather than on rights of individuals to be included in dialogue; thirdly, the need for patience and commitment in the recognition that responsibilities to others are fulfilled over the long, rather than the short, term; and, finally, the idea of care ethics as a substantive, democratic ethic of responsibility. These arguments emerge out of the basic ideas of care ethics — that relations and responsibilities of care are central to human life, and that care is a public value that must be negotiated at a variety of levels, from the household to the international community.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.093 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".