Bibliographic record
Abstract
This article traces John Rawls’s debt to Frank Knight’s critique of the ‘just deserts’ rationale for laissez-faire in order to defend justice as fairness against some prominent contemporary criticisms, but also to argue that desert can find a place within a Rawlsian theory of justice when desert is grounded in reciprocity. The first lesson Rawls took from Knight was that inheritance of talent and wealth are on a moral par. Knight highlighted the inconsistency of objecting to the inheritance of wealth while taking for granted the legitimacy of unequal reward based on differential productive capacity. Rawls agreed that there was an inconsistency, but claimed that it should be resolved by rejecting both kinds of inequality, except to the extent they benefitted the worst off. The second lesson Rawls learned from Knight was that the size of one’s marginal product depends on supply and demand, which depend on institutional decisions that cannot themselves be made on the basis of the principle of rewarding marginal productivity. The article claims that this argument about background justice overstates its conclusion, because the dependence of contribution on institutional setup is not total. Proposals for an unconditional basic income may therefore have a strike against them, as far as a reciprocity-based conception of desert is concerned. If we follow Knight’s analysis of the competitive system, however, so too does the alternative of leaving determination of income up to the market.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".