28 times feminist joke lists were real AF: feminist humour and the politics of joke lists
Bibliographic record
Abstract
Despite the pervasive visibility of joke lists in online popular culture, the latter remains a surprisingly neglected site of scholarly inquiry. Feminist joke lists represent the concerted efforts of online content producers to curate a wide range of feminist humour—content that is expressly feminist in political orientation and/or sympathetic to highlighting feminist issues and sensibilities. These lists offer a compelling point of departure for interrogating the uses, limitations, and possibilities of joke lists for feminist communities of practice and how this cultural form enacts or inscribes feminist politics online. In this essay, I theorize the political significance of feminist joke lists through an examination of 20 distinct lists featuring over 350 jokes spanning a six-year period (2013–2019). Through an examination of general curated feminist joke lists, as well as humour lists produced in the wake of the 2017 and 2018 Women’s Marches, I argue that these broader activities contribute to the visibility and validation of feminist humour, the sharpening of feminist critique, and the solidarity of feminist communities.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".