This is Me, This is Who You Think I Am: Disgust and the Liminal Agency of Young Adolescents
Bibliographic record
Abstract
This thesis is an exploration of liminal teen agency in Heather O’Neill’s Lullabies for Little Criminals and Raziel Reid’s When Everything Feels Like the Movies. By focusing on two teen characters from working class families, one female, one queer, I investigate how teens assert their autonomy while still living under the constraints of classism and (hetero)sexism. While these teens are able to retain some form of autonomy, I argue that their agency is often obscured or overwritten by the disgust reactions of other characters in each novel. Drawing on affect theory, particularly Sara Ahmed’s body of work, Jonathan Dollimore, and Sianne Ngai, and drawing on Joan Sangster’s work on the construction of female delinquency, I investigate the significance of the disgust reaction, and how the reaction is a means of reasserting power over the willful, resistant teen body. As the Canada Reads competition reveals, the middle class, cis-hetero readerly discomfort with these novels becomes an avenue through which this literature is deemed “unpalatable,” providing a justification to doubt the testimony of narrators like Baby and Jude. This thesis is ultimately an intervention into doubted testimony, and demonstrates how affective disgust is the source of doubt. Since agency and testimony are tightly intertwined in each novel, doubting testimony becomes a violent form of denying these characters, and the authors, agency.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".