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 best available … about the nature of (WHE, 184). Since its publication, Sumner'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 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".