Bibliographic record
Abstract
This thesis examines the use of diegetic pre-composed music in three American lesbian feature films.Numerous trends can be noted in the selection of music in lesbian film broadlymusic is often selected to draw on insider knowledge of the target audience of these films, creating cachet.This cachet comes with accompanying "affiliating identifications" (Kassabian 2001) that allow music to be used in the films' construction of characters' identities.This is evident in the treatment of music in many lesbian films: characters frequently listen to, discuss, and perform music.This thesis focuses specifically on riot grrrl music and the closely linked genre of queercore (which together Halberstam ([2003] 2008) refers to as "riot dyke") in three films that focus on the lives of young women: The Incredibly True Adventure of Two Girls in Love (dir.Maria Maggenti, 1995), All Over Me (dir.Alex Sichel, 1997), and Itty Bitty Titty Committee (dir.Jamie Babbit, 2007).By examining how diegetic riot dyke music is used in these three films to build characters' identities and contribute to the films' narratives I argue that in these films, riot dyke music is presented as being central to certain queer identities and communities, and that this music (as well as the community that often accompanies it) is portrayed as instigating or providing opportunities for characters' personal growth and affirming characters' identities in adverse environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".