“WAVING THE BANNER OF DEMOCRACY”: DEMOCRATIC SANCTIONS AND THREE HYPOCRISY PUZZLES
Bibliographic record
Abstract
Abstract This essay aims to advance the general discussion of hypocrisy in moral and political philosophy as well as normative policy debates regarding democratic sanctions against autocracies that often trigger charges of hypocrisy. In the process of making sense of these charges, I articulate and tackle three general puzzles regarding hypocrisy complaints. The first—the inaction puzzle—asks why a charge of hypocrisy should have any effect on the moral assessment of an agent’s actions, as distinct from the agent’s character or attitudes. The second—the ambivalence puzzle—asks why we often react to hypocrisy charges with seemingly paradoxical ambivalence, recognizing such charges for the transparent deflections they often are, but also granting their normative force. The third—the preemption puzzle—asks why hypocrisy charges do not entirely lose their force when their targets openly concede that they too have suffered from the same flaws that they highlight in others. I argue that sustained reflection on each of these puzzles can enrich—and be enriched by—normative analysis of democratic sanctions.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".