Economic Metaphors and Equivocation: The Unfortunate Similarities of Rawls and Nozick
Bibliographic record
Abstract
Liberalism always finds itself trapped in paradox, privileging individual rights and value-pluralism while simultaneously assuming an overarching conception of the good in order to defend and protect this pluralism. This paper looks at how Rawls and Nozick, the two most important and most different 20th century liberal philosophers, used a similar reliance on economic metaphor and logic in order to cover up this paradox. I argue that this invocation of economic metaphor does not solve the problem, but rather hides it from immediate view through equivocation. By considering the type of work economic reasoning can do in a political theory, I suggest why economic metaphor was so useful, and perhaps necessary, for these thinkers.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| 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".