Bibliographic record
Abstract
The charge of hypocrisy is a peculiar kind of accusation: it is damning and ubiquitous; it is used to deny the hypocrite standing to speak; and it is levelled against a great variety of conduct. Much of the philosophical literature on hypocrisy is aimed at explaining why hypocrisy is wrongful and worthy of censure. We focus instead on the use of the accusation of hypocrisy and argue for a revisionary claim. People think that hypocrisy in politics is bad and that calling it out is good. Our novel claim is that even if hypocrisy in politics is bad (and that is a big if), calling it out is worse. We give a feminist case as to why accusations of hypocrisy are problematic. We also go further and claim that hypocrisy is a ubiquitous and perhaps even a necessary and beneficial part of political debate in liberal democracies. We also consider and reject candour as a possible alternative solution to hypocrisy in public debate. We argue that requiring people to be candid is not necessarily a good solution because it will often require one to divulge what is private when there are good reasons not to do so.
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.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.068 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".