The Stereotypical Gag in Carlo Emilio Gadda’s and Pietro Germi’s Detective Stories
Bibliographic record
Abstract
This article analyzes two comic incidents portrayed in Gadda’s That Awful Mess on the Via Merulana and its adaptation The Facts of Murder by Pietro Germi. We argue that the inclusion of what we term ‘stereotypical gags’ in the detective novel functions as a modernist device that brings about a distancing of the audience and acts to distort the boundaries of genre. We show how the stereotypical gag both foregrounds and backgrounds the authorial humorous agency and results in satire and parody, respectively. In the first case, the authorial agency explicitly endorses humorous clichés and mocks along with the audience’s societal conventions. In the second case, humour generates a less obvious incongruity with respect to the discourse of the genre, resulting in parody through which the authorial agency mocks the audience and its trust in the values of the traditional detective story.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".