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Record W2262416507 · doi:10.1353/bkb.2016.0008

Suicide Prevention in Nêhiyawi (Cree) Comic Books

2016· article· en· W2262416507 on OpenAlexaboutno aff
Judith Leggatt

Bibliographic record

VenueBookbird/Book bird · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsComicsStorytellingNarrativeDepictionCommitIndigenousPopulationHistoryOral traditionAutonomySociologyMedia studiesLiteratureGender studiesArtAnthropologyDemographyPolitical scienceLaw

Abstract

fetched live from OpenAlex

One of the many negative effects of colonization in North America is the epidemic of suicide infecting Indigenous youth; in Canada, First Nations youth living on reserve are five to six times more likely to commit suicide than is average for the general population. This paper examines the depiction of suicide in two Nêhiyawi (Cree) comic books: Darkness Calls by Steven Keewatin Sanderson (2006) and 7 Generations: A Plains Cree Saga by David Alexander Robertson and Scott B. Henderson (2012). The formal conjunction of oral and graphic storytelling in these two works emphasizes the necessity of bringing Nêhiyawi history and tradition into the contemporary world, and the interrelation of the two genres parallels the relationships between community and individual that are inherent to health. Just as the graphic novel form interacts with oral storytelling, adding new dimensions, but not replacing it, so the strength of the individual draws from and contributes to the strength of the nation. The comic books work not only through overt anti-suicide messages but also through storytelling strategies that connect the present to ongoing traditions, graphic novels to oral storytelling, and individual autonomy to community strength.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.011
GPT teacher head0.228
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations1
Published2016
Admission routes1
Has abstractyes

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