Are Second Person Needs ‘Burdened Virtues’?: Exploring the Risks and Rewards of Caring
Bibliographic record
Abstract
This essay contributes to the ethics of vulnerability and to the tradition of feminist care ethics by introducing the notion of second-person needs. Employing the work of Annette Baier, who argues that we are all ‘second persons’ insofar as personhood arises through a childhood in the care of others, it draws attention to the needs that are illuminated when we approach ourselves and others as second persons, and makes a case for the moral import of second-person needs. In drawing from and critically responding to Lisa Tessman’s concept of ‘burdened virtues,’ it also adds to a growing field of ethical work on moral damage. In particular, this paper reminds readers of the benefits of the virtue of sensitivity and attention to other’s suffering, without ignoring the toll that it can extract.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".