Bibliographic record
Abstract
Skim (2008) by Mariko Tamaki and Jillian Tamaki is a semi-graphic memoir based on the coming-of-age story of Kimberly Keiko Cameron, a Japanese-Canadian queer girl whose nickname is Skim. Skim is 16 years old and attending a Catholic girls’ high school in Toronto in the 1990s, where most students are white and thin. Due to her non-whiteness, queerness, fatness, depression, and interest in Wicca, Skim experiences discrimination, marginalization, and exclusion at school, which lead to daily trauma for her. Trapped within in her loneliness, Skim eats as a defense mechanism against the normalized daily trauma. This eating, in a vicious circle, leads to more fatness and causes her to become an object of ridicule and stigmatization, ultimately leading to suicidal depression. Skim is singled out as a person who is the most likely to commit suicide at school and thus faces institutional control, surveillance, discipline, and oppression led by her teachers and peers, who consider Skim’s fatness (a physical disability) and depression (a mental illness) to be an individual problems to be fixed or eliminated with the help of school-based suicide prevention programs. In this paper, I will examine how Skim helps readers understand youth suicidal depression and eating disorders in connection with sexism, ableism, racism, sanism, and heterosexual normativity, all of which are deeply rooted in Canadian society. In addition, I show how Skim, presented in a diary format, resists the triumphant narrative of “getting better” and ultimately contributes to eliminating the prejudice that young people marginalized due to fatness, queerness, non-whiteness, and suicidal depression face, making them powerless, passive, and voiceless in Canadian society.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".