‘I want to delete this tweet so much, but…’: Jameela Jamil as a celebrity feminist educator
Bibliographic record
Abstract
The ideas of outspoken feminist celebrities are met with scepticism. This scepticism is rooted in the idea that, while celebrities have a platform for expression, they are not academics and their role in education should therefore be limited. This article explores the role of Jameela Jamil, a British, queer actor, and analyses her use of Instagram and Twitter as platforms for education and social change. I argue that she uses social media to teach and learn from her followers regarding body acceptance, racial and sexual inclusivity and queer representation. This work also explores the realities of clapbacks, cancel culture, mistake-making, shame culture and affective solidarity via her use of language, such as through the vulnerable phrase ‘I want to delete this tweet so much, but…’. In positioning Jamil as more than simply a celebrity feminist, and beyond what is considered a normative public intellectual, I assert that she embodies the role of a celebrity feminist educator. This role is unique as it creates space for Jamil’s online feminist activism, her accessible use of language and her desire to teach and learn from her followers to be made meaningful within the context of feminist education and celebrity studies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".