Persons and Their Well-Being: A Critical Discussion of Kaplow and Shavell's Fairness Versus Welfare
Bibliographic record
Abstract
At the heart of any legal policy decision is a set of value judgments about the state of the world we live in. Justice, fairness and social welfare are concepts that play varying roles in these decisions. Welfare economics, a subset of economic theory, imparts a normative framework to the decisions, by examining economic activities at an individual level, in an attempt to maximize aggregate social welfare.As distinguished researchers in the field of economic analysis, Louis Kaplow and Steven Shavell have generated a plethora of debates with the release of Fairness versus Welfare. Their central claim is the proposition that the sole decision-making criterion for creating legal policy must be aggregate well-being; other factors, such as fairness or justice, will bring an undesirable result. Accordingly, individuals are not seen as holders of rights, but as depositories of well-being.The author challenges the validity of Kaplow and Shavell's thesis on a number of grounds. First, their claim requires a clear conception of individual well-being. But in his view it does not; accordingly, their central premise is undermined. Second, he argues that the authors define fairness in a circular way that supports their thesis; that is, they define it as anything that does not increase aggregate welfare. Finally, the author notes that Kaplow and Shavell purport to care about individual well-being but are willing to sacrifice any individual in order to achieve a higher aggregate amount of well-being--in effect, treating individuals as things, or means to an end, rather than as the beneficiaries of the exercise.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.020 | 0.093 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.018 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".