THE FITTING-ATTITUDE ANALYSIS OF VALUE RELATIONS AND THE PREFERENCES VS. VALUE JUDGEMENTS OBJECTION
Bibliographic record
Abstract
Abstract: According to Wlodek Rabinowicz's (2008) fitting-attitude analysis of value relations, two items are on a par if and only if it is both permissible to strictly prefer one to the other and permissible to have the opposite strict preference. Rabinowicz's account is subject, however, to one important objection: if strict preferences involve betterness judgements, then his analysis contrasts with the intuitive understanding of parity. In this paper, I examine Rabinowicz's three responses to this objection and argue that they do not succeed. I then propose an alternative solution. I argue that the objection can be avoided if we ‘relativize’ Rabinowicz's account and define parity in terms of opposite strict preferences between two items that are only relatively permissible, rather than permissible simpliciter. I argue that this account of parity can be defended if we take seriously the distinction between sufficient and decisive reason for a preference relation. I also show that, on the basis of this distinction, we can arrive at a more extensive taxonomy of value relations than the one proposed by Rabinowicz.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".