Bibliographic record
Abstract
Thomas Hobbes’s infamously severe accounts of the phenomenon of laughter earned the condemnation of such varied readers as Francis Hutcheson and Friedrich Nietzsche, and he has maintained his reputation as an enemy of humor among contemporary scholars. A difficulty is raised by the fact that Hobbes makes ample use of humor in his writings, displaying his willingness to evoke in his readers what he appears to condemn. This article brings together Hobbes’s statements on laughter and comedic writing with examples of his own humorous rhetoric to show that Hobbes understands laughter as a species of insult, but that there are conditions under which humor can be made to serve the cause of peace. Drawing on evidence from across Hobbes’s works, and in particular from an understudied discussion of “Vespasian’s law” in the Six Lessons, this essay theorizes the conditions under which Hobbes found witty contumely to be conducive to peace. On this reading, Hobbes models the discreet use of humorous rhetoric in defense of peace, a defense that will be ongoing even after the commonwealth has been founded. Hobbes offers insight into how we can remain attuned to laughter’s inegalitarian tendencies without foregoing the equalizing potential to be found in laughing at ourselves and at those who think too highly of themselves.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.065 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| 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".