Bibliographic record
Abstract
Ahmed Masoud’s The Shroud Maker packs a powerful punch in using the implicative potential of black comedy as a site of resistance. Masoud deploys black comedy to resist and assert control over the exploitative discourse and action against the Palestinians living in Gaza. He does this by using black humour as a tool of alienation to detach people from their trauma, break the fourth wall to make the audience complicit, as a weapon to challenge Israeli dominant discourse, and to assert Palestinian control over the war’s narrative. Dark humour walks the thin line between humour and conflict in the play where “battered by injustice, but still defiant,” the eighty-four-year-old protagonist, Hajja Souad, a shroud maker by profession, introduces trauma through humour and then tips the balance in favour of trauma. Through her offensive jokes and satiric thrust, the play confronts the desensitisation of violence against Palestinians by evoking a reaction in the audience—laughter, followed by reflection on their laughter. While the balancing act between humour and trauma is maintained, The Shroud Maker argues that humour is perhaps the most effective way of communicating and ascribing responsibility to its spectators. Ahmed Masoud puts forth an alternative way of discussing political violence and war beyond the dominant western narrative that marginalises the Palestinian voice. Relaying the tragedy of violence and culture wars through dark humour underscores the message of resistance, coping and persistence in Palestine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".