Que(e)rying Youth Suicide: Sexism, Racism, and Violence in Skim and 13 Reasons Why
Bibliographic record
Abstract
This paper troubles positivist and pathological discourses surrounding youth suicide through critical engagement with young adult fiction: Skim and 13 Reasons Why. These texts offer opportunities for readers to dwell on and question youth suicide prevention and intervention through an engagement with affect, gender, queerness, and race. Skim (2008, Groundwood) and 13 Reasons Why (2017) counter ‘it gets better’ stories: they interrogate the inevitability of bullying, question the predictable approaches that schools take in their response to violence, and assert that the issue at hand is more systematic. Together, these analytics que(e)ry youth suicide by asking: how does the biopolitics (or necropolitics) of livability fit into popularized understandings of youth suicide? Read together, Skim and 13 Reasons Why provide opportunities to meaningfully question livability through the characters of Skim and Courtney—two Asian girls who bear the brunt of racist and sexist violence. Skim becomes a ‘project’ of white girls’ anti-suicide campaign and Courtney is barely living as she attempts to secure the plaform of ‘model minority.’ Both girls are queer, too. In its entirety, this paper arguse that popularized models of suicide intervention continue to ignore the pressing needs of queer Asian girls—such as Skim and Courtney.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".