Bibliographic record
Abstract
I discuss Young’s “asymmetrical reciprocity” and apply it to an ethics of mental health care. Due to its emphasis on engaging with others through respectful dialogue in an inclusive manner, asymmetrical reciprocity serves as an appropriate framework for guiding caregivers to interact with their patients and to understand them in a morally responsible and appropriate manner. In Section 1, I define empathy and explain its benefits in the context of mental health care. In Section 2, I discuss two potential problems surrounding empathy: the difficulty of perspective-taking and “compassion fatigue.” In Section 3, I argue that these issues can be resolved if examined through the lens of an ethics of care. Reciprocal relationships between patients and caregivers are an important element in the development of an ethics of care. In Section 4, I introduce two models of reciprocity that can be applied to a health care context: Benhabib’s symmetrical reciprocity and Young’s asymmetrical reciprocity. In Section 5, I demonstrate how asymmetrical reciprocity cultivates empathy and, in Section 6 and Section 7, I show how it overcomes the objections of empathy and improves therapeutic relationships.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".