A labour of love: cancel culture as an accountability practice in the comedy industry
Bibliographic record
Abstract
Dominant power structures shape the way that knowledge and affect produces form and content in comedy. This includes the blatant leveling of sexualized violence, racism and discrimination against people with diverse identities who are both the ‘subject’ of comedic performance as well as workers in the industry. Those with power in the comedy industry often resist taking accountability for their violent behaviour through the articulation of ‘cancel culture’. In this paper we expand current understandings of political comedy to include the personal, specifically the use of lived experience as a political strategy for accountability. We argue that by using personal experiences of violence and discrimination as material, affected comedy workers can more publicly hold their peers accountable. We further disentangle comedians’ demands for safety from cancel culture’s prescient framing as unfounded ‘wokeness’ wielded against comedy’s affable heroes. We argue that making jokes that call attention to a comedian’s bad behaviour is a form of work and a labour of love that targeted comedy workers do to keep their industry safe.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".