Bibliographic record
Abstract
Abstract Are demands for equality motivated by envy? Nietzsche, Freud, Hayek, and Nozick all thought so. Call this the Envy Objection. For egalitarians, the Envy Objection is meant to sting. Many egalitarians have tried to evade the Envy Objection. But should egalitarians be worried about envy? In this article, I argue that egalitarians should stop worrying and learn to love envy. I argue that the persistent unwillingness to embrace the Envy Objection is rooted in a common misunderstanding of the nature of the charge, what it reveals, and what can be said in response to it. I develop what Bernard Williams might call a vindicatory genealogy of envy, thereby allowing us to see that envy, rather than undermining egalitarian intuitions, can in fact play a distinct justificatory role (when it is fitting), which undermines the Envy Objection.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".