Bibliographic record
Abstract
In his article "'Mad Laughter' in Federman's The Twofold Vibration" Menachem Feuer discusses one of the central questions in the debate over post-Holocaust representation with regard to comedy and laughter. Several authors and filmmakers including Mel Brooks, Lina Wertmüller, Roberto Benigni, Michael Chabon, or Jonathan Safran Foer employ comedy in work. Although the books and films of these authors and filmmakers certainly test the limits of representation through the use of comedy in post-Holocaust art, the use of "mad laughter" in the work of Raymond Federman to represent the Holocaust stands out as the most important exploration of post-Holocaust comedy today. Feuer argues that Federman's text traverses the fine line between a self-referential text, which alludes only to itself and not to any extrinsic historical referent (such as the Holocaust) and a form of laughter that is intimately connected to the trauma of the Holocaust. Further, the novelty of Federman's textual forays is the simultaneous exaltation of the self-referential to the level of what Susan Sontag would call camp style and the rigorous awareness of historical trauma. Federman shows readers that "mad laughter" can preserve history and a self-referential sensibility which sees itself as textual and desires to recreate itself through (inter)textuality.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".