Getting “Woke” With Each Joke: Black Comediennes and Representational Resistance on YouTube
Bibliographic record
Abstract
This research looks at the relationship between comedy, alternative media, and representations of Blackness. Using case studies Akilah Hughes and Franchesca Ramsey, two Black comediennes on YouTube, this thesis asks: how do Black women use both political comedy and alternative media to challenge the stereotypical and racialized representations of themselves in traditional media? A theoretical framework of critical race studies, post-colonialism, intersectional and Black feminisms, postmodernism, and theories of comedy in conjunction with the thematic qualitative text analysis of 30 YouTube videos were used to answer the question. My findings determined that Black women use political comedy and alternative media platforms to satirize oppressive and discriminatory ideologies and behaviours, framing instances of every day racism in absurd and exaggerated terms, which ultimately provides nuanced representations of Black womanhood that affirm the sexist, racist, and racially charged experiences and microaggressions that Black women endure on a daily basis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".