Bibliographic record
Abstract
Siegel (English and American studies, Washington State Univ., Vancouver) counters the dearth of research into goth and the hostility of post-Columbine representations with a sympathetic, compelling examination of goth subculture as premised on gender fluidity, with sadomasochistic practices as 'radical technologies of resistance.' This argument is strongly informed by French theorists Gilles Deleuze and Felix Guattari, presupposing some familiarity with their arguments. To this end, goth is a revolutionary mode of becoming in the face of alienating culture. Methodologically, Siegel draws loosely from many online discussions with goths, but more so on portrayals of goth through music, novels, and cinema, including Boys Don't Cry and The Matrix. For example, Siegel examines Poppy Z. Brite's novels to foreground the male hero as a masochist challenging the gender binary by 'queering' masculinity. Siegel also challenges perceptions of goth racism with attention to Asian American youths involved in goth. This book is sometimes difficult to follow. Chapter one opens with gender and sexuality in goth-related music and closes with a critique of abstinence-only education. Linkages between such elements depend on careful readers; such readers will be rewarded with a provocative analysis of the challenge and resistance goth desire represents within 'America's culture of denial.' Summing Up: Recommended. Upper-division undergraduates and above.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".