The limitations of ethnic humour: can ethnic humour function as an anti-racist discourse?
Bibliographic record
Abstract
This thesis examines in what ways, if any, the use of ethnic humour can be used to promote anti-racist discourse. Presently, in Canada, ethnic humour is "in". As a result, comedians of minority backgrounds are being recognized and celebrated in the entertainment industry and, particularly, in comedy clubs. Among other comedians, East Indian-Canadian comic Russell Peters has articulated that he uses his comedy to draw out and break down the stereotypes that exist within our society. However, given the aggressive and unstable nature of stand-up comedy and the way its role and the role of comedians in Canadian society are positioned via the media and the war metaphor used to discuss it, it will become evident that a complex site such as a comic's stage is not as conducive to anti-racist work as many seem to believe.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".