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Record W4403186878 · doi:10.7592/ejhr.2024.12.3.929

Puns and pain in Palestine

2024· article· en· W4403186878 on OpenAlexaff
Natasha W. Vashisht

Bibliographic record

VenueEuropean Journal of Humour Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPalestinePunHistoryArtAncient historyLiterature

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.409
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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