Friedman, Harsanyi, Rawls, Boulding - or Somebody Else?
Bibliographic record
Abstract
This paper investigates distributive justice using a fourfold experimental design : The ignorance and the risk scenarios are combined with the self-concern and the umpire modes. We study behavioral switches between self-concern and umpire mode and investigate the goodness of ten standards of behavior. In the ignorance scenario, subjects became on average less inequality averse as umpires. A within-subjects analysis shows that about one half became less inequality averse, one quarter became more inequality averse and one quarter left its behavior unchanged as umpires. In the risk scenario, subjects become on average more inequality averse in their umpire roles. A within-subjects analysis shows that half of them became more inequality averse, one quarter became less inequality averse, and one quarter left its behavior unchanged as umpires. As to the standards of behavior, several prominent ones (leximin, leximax, Gini, Cobb-Douglas) experienced but poor support, while expected utility, Boulding's hypothesis, the entropy social welfare function, and randomization preference enjoyed impressive acceptance. For the risk scenario, the tax standard of behavior joins the favorite standards of behavior.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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; both teacher heads agree on what is shown here.
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".