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Record W3000252329 · doi:10.5130/csr.v25i2.6182

Que(e)rying Youth Suicide: Sexism, Racism, and Violence in Skim and 13 Reasons Why

2019· article· en· W3000252329 on OpenAlexaff
Jocelyn Sakal Froese, Cameron Greensmith

Bibliographic record

VenueCultural Studies Review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsQueerRacismSociologyIntervention (counseling)Gender studiesPsychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.854
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.315
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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