Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it