Bibliographic record
Abstract
Following Andrew Jason Cohen, Lucia Rafanelli construes toleration to consist in not merely limiting one’s interference with others’ behaviour, but doing so because of a principled commitment to respecting others’ independent choices. I argue that this conflates toleration with distinctly liberal ideals such as freedom of conscience or autonomy. This conflation not only impoverishes our conceptual vocabulary by using ‘toleration’ to label concepts or phenomena for which there are already perfectly good words, it also renders non-liberal conceptions or theories of toleration nonsensical by definitional fiat. This in turn risks making the historical emergence of theories and practices of toleration on purely prudential grounds – namely, reasons of state – unintelligible. Even though this does not, I think, compromise Rafanelli’s substantive normative conclusions about interference, it does raise important questions about the proper relation between contemporary political theory and the history of political thought.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".