Bibliographic record
Abstract
Care theorists have yet to outline an account of how the concept of toleration should function in their normative framework. This lack of outline is a notable gap in the literature, particularly for demonstrating whether care ethics can appropriately address cases of moral disagreement within contemporary pluralistic societies; in other words, does care ethics have the conceptual resources to recognize the disapproval that is inherent in an act of toleration while simultaneously upholding the positive values of care without contradiction? By engaging care ethics with John Locke’s (1632–1704) influential corpus on toleration, I answer the above question by building the bases for a novel theory of toleration as care. Specifically, I argue that care theorists can home in on an oft-overlooked aspect of Locke’s later thought: that the possibility of a tolerant society is dependent on a societal ethos of trustworthiness and civility, to the point where Locke sets out positive ethical demands on both persons and the state to ensure this ethos can grow and be sustained. By leveraging and augmenting Locke’s thought within the care ethical framework, I clarify how care ethics can provide meaningful solutions to moral disagreement within contemporary pluralistic societies in ways preferable to the capability of a liberal state.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.067 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.023 |
| 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".