Bibliographic record
Abstract
Bernard Mandeville argued that traits that have traditionally been seen as detrimental or reprehensible, such as greed, ambition, vanity, and the willingness to deceive, can produce significant social goods. He went so far as to suggest that a society composed of individuals who embody these vices would, under certain constraints, be better off than one composed only of those who embody the virtues of self-restraint. In the twentieth century, Mandeville's insights were taken up in economics by John Maynard Keynes, among others. More recently, philosophers have drawn analogies to Mandeville's ideas in the domains of epistemology and morality, arguing that traits that are typically understood as epistemic or moral vices (e.g. closed-mindedness, vindictiveness) can lead to beneficial outcomes for the groups in which individuals cooperate, deliberate, and decide, for instance by propitiously dividing the cognitive labor involved in critical inquiry and introducing transient diversity. We argue that mandevillian virtues have a negative counterpart, mandevillian vices, which are traits that are beneficial to or admirable in their individual possessor, but are or can be systematically detrimental to the group to which that individual belongs. Whilst virtue ethics and epistemology prescribe character traits that are good for every moral and epistemic agent, and ideally across all situations, mandevillian virtues show that group dynamics can complicate this picture. In this paper, we provide a unifying explanation of the main mechanism responsible for mandevillian traits in general and motivate the case for the opposite of mandevillian virtues, namely mandevillian vices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".