Re-fashioning stories through feminist filmmaking, an interview with Samita Nandy
Bibliographic record
Abstract
To conclude this Special Issue ‘Re-Fashioning Stories for Celebrity Counterpublics’ of the Journal of Applied Journalism & Media Studies (AJMS), I am delighted to share an interview with Samita Nandy, celebrity scholar, filmmaker and director of the Centre for Media and Celebrity Studies (CMCS). Her research focuses on the cultural dimensions of fame, with a specific interest in celebrity activism, storytelling and the performance of authenticity and intimacy in glamorous narratives. In addition to her academic work, Nandy is also a certified broadcast journalist from Canada and media critic. I had the opportunity to assist her and Kiera Obbard with the organization of the 8th CMCS Conference, which inspired this Special Issue. This interview is thus an opportunity to further expand our reflection on the political possibilities of storytelling and celebrity counterpublics. Our discussion builds on the themes and arguments developed throughout this issue to further explore what popular storytelling means in practice. She reflects on her engagement with celebrity culture and life-writing in her feminist research and artistic endeavours, and how it has empowered her to tell personal and collective stories. The interview format and its themes provide a unique opportunity to contemplate the affordances of a reflective practice paradigm and the artistic applications of disciplinary knowledge, one which bridges academic work with media professions, and which we hope will resonate with AJMS readers.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".