Bibliographic record
Abstract
This chapter provides a brief overview of the representation of Lesbian, gay, bisexual, transgender, and questioning (LGBTQ) people in U.S-based media. It considers the role of different media technologies—printed text, film and TV, and digital and mobile media—in helping LGBTQ people locate and form communities. In the US and Canada, common stereotypes of LGBTQ people include old theories that non-normative identities and behaviors come from psychological problems. For most of the twentieth century, mainstream news media, when they bothered to report on LGBTQ people, offered an outside perspective, one that often depicted LGBTQ communities as problematic, odd groups in society. While the gay press helped make LGBTQ communities publicly visible, advertising turned them into a recognizable market. Pinkwashing presents an image of LGBTQ communities as "respectable," matching the image of the status quo: white, cisgender men with disposable incomes. While stereotypes can have negative cultural and political implications, they have sometimes been useful for LGBTQ people in film.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.594 | 0.487 |
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".