Scales for Scope: A New Solution to the Scope Problem for Pro-Attitude-Based Well-Being
Bibliographic record
Abstract
Theories of well-being that give an important role to satisfied pro-attitudes need to account for the fact that, intuitively, the scope of possible objects of pro-attitudes seems much wider than the scope of things, states or events that affect our well-being. Parfit famously illustrated this with his wish that a stranger may recover from an illness: it seems implausible that the stranger's recovery would constitute a benefit for Parfit. There is no consensus in the literature about how to rule out such well-being-irrelevant pro-attitudes. I argue, first, that there is no distinction in kind between well-being-relevant and irrelevant pro-attitudes. Instead, well-being-irrelevant pro-attitudes are the limiting cases on the scale measuringhow muchof a difference pro-attitudes make to the subject's well-being. Second, I propose a particular scalar model according to which the well-being-relevance of pro-attitudes is measured either by their hedonic tone, or by the subject's conative commitment.
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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 | 0.023 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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".