‘Queering’ TV, one character at a time: How audiences respond to gender-diverse TV series on social media platforms
Bibliographic record
Abstract
This article builds further on research in gender/queer TV studies to understand how interpretive communities form around gender-diverse TV series on social media platforms, while questioning the influence a broadcaster/content provider may still have on the reception of LGBTQ characters. Since recent technological innovations have deeply upset normative definitions of television and of its ‘identity’, this article seeks to understand whether the inclusion of LGBTQ characters in TV series has a similar potential to encourage viewers to queer or challenge normative knowledges about human sexualities and identities. To this end, the article provides a qualitative analysis of discourses that have been published on the official Facebook page of two US serialised dramas: Sense8 (Netflix 2015–2018) and Billions (Showtime 2016–). This research reveals that conversations around Sense8 and Billions differ significantly, ranging from a tendency to deflect criticism and promote progressive readings of the show ( Sense8), to more aggressive debates and frequent attempts to ‘solve’ gender ambiguities ( Billions). Through a detailed analysis of comments and interactions on a popular social media platform, this article, therefore, argues that the nature of a particular content provider might still affect – though never determine – the formation of interpretive communities online, and the nature of comments published around a series featuring LGBTQ character(s).
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".