Feminist humour’s disruptive potential: Trevor Noah’s Born a Crime: Stories from a South African Childhood and Rupi Kaur’s ‘I’m taking back my body’
Bibliographic record
Abstract
Using Trevor Noah’s Born a Crime: Stories from a South African Childhood and Rupi Kaur’s TEDxKC performance, ‘I’m taking back my body’, as case studies, this article examines how feminist humour is used by celebrities and public intellectuals to tell personal stories of oppression, trauma and inequality. Building on humour theory, feminist humour theory and affect theory, this article examines the potential of feminist humour as a rhetorical device to help storytellers tell difficult stories, to engage in acts of community-building and world-making, to challenge social inequalities and to enable social change. Ultimately, this article asks what we can learn from these examples, and how we can employ feminist humour in our own storytelling practices not only to disrupt power relations and establish solidarity, but also to imagine new, more equitable, worlds.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.034 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".