Structural functions of the targeted joke: Iranian modernity and the Qazvini man as predatory homosexual
Bibliographic record
Abstract
Abstract Focusing on the disciplinary function of humor as an understudied subject in humor studies, this article addresses Qazvini jokes – the contemporary Persian joke cycle targeting men from the Iranian city of Qazvin – as mainstream gender humor that uses ridicule as a means of supporting heteronormativity and fueling homophobia. Adopting an historical-analytical approach and considering examples of and references to Qazvini jokes, I argue that the joke series likely originated as a disciplinary tool to buttress the emerging heteronormative gender order of early modern Iranian society. Contextualized instances of these jokes not only illustrate that this punitive function has endured to the present but also indicate their ongoing homophobic role. This argument problematizes the claim made in humor studies that jokes have no social consequences. Qazvini jokes may be inconsequential in the limited sense that they might not affect attitudes toward their direct targets – that is, individual men of Qazvin – yet their heteronormalizing and homophobic functions clearly speak to larger social structures within Iranian society and culture. This form of ethnic humor both adheres to and informs Iran’s prevailing gender and sexuality norms; as such, in this broader sense, the jokes may indeed have far-reaching consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".