Contagious Laughter and the Burlesque: From the Literal to the Metaphorical
Bibliographic record
Abstract
Georges Bataille insisted on the role contagion plays in laughter, an emotional experience in which he discerns ‘the specific form of human interattraction’.1 The philosopher distinguishes between a mediated interattraction and a more immediate one, the first being linked to the presence of a ‘trigger’ (in this case the comical object), the second to the psychosociological permeability that favours ‘contagion’ or ‘sympathie’ : Les organismes semblables sont susceptibles, dans de nombreux cas, d’être traversés par des mouvements d’ensemble : ils sont en quelque sorte perméables à ces mouvements. Je n’ai d’ailleurs fait ainsi qu’énoncer en d’autres termes le principe bien connu de la contagion, ou si Ton veut encore de la sympathie, mais je l’ai fait je crois avec une précision suffisante. Si l’on admet la perméabilité à des mouvements d’ensemble, à des mouvements continus, le phénomène de la reconnaissance apparaîtra construit à partir du sentiment de perméabilité éprouvé en face d’un autre/socius. [Like organisms, in many instances, may well experience group movements. They are somehow permeable to such movements. What is more, I have thus only stated in other terms the well-known principle of contagion, or if you still want to call it that, fellow feeling, sympathie , but I believe I have done this with sufficient precision. If one acknowledges permeability in ‘group movements’, in continuous movements, the phenomenon of recognition will appear to be constructed on the basis of the feeling of permeability experienced when confronted with an other/socius.] 2 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".