Rehabilitating L.W. Sumner's 'Happiness Theory of Welfare' - Part 1: Sumner's welfare theoretic system
Bibliographic record
Abstract
In philosophy, theories of welfare's nature abound. One of these is Canadian moral philosopher L.W. Sumner's (subjective) 'happiness theory of welfare', which he argues in his 1996 book "Welfare, Happiness, and Ethics' (or "WHE' for short) is "the best available - about the nature of welfare" (WHE, 184). Since its publication, S.umner's theory of welfare has attracted a range of criticisms, such that it is now widely (though I would argue wrongly) regarded as falling well short of being "the best available". This paper contends that criticisms of Sumner's 'happiness theory of welfare' misinterpret or misunderstand the welfare theoretic system presented in WHE (explicated here in terms of that system's implicit as well as explicit details). The totality of the implicit and explicit details of Sumner's welfare theoretic system is 'what Sumner's really saying in WHE' about welfare's nature, which is more detailed and 'determined' than is currently appreciated in the philosophical literature. This paper lays the groundwork for a reappraisal (in a follow-up paper) of Sumner's 'happiness theory of welfare' as a viable candidate for "the best available theory" of welfare's nature.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".