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Record W3039734244 · doi:10.1093/fmls/cqaa021

Canadian Noir: Consumer Culture, Colonial Nationalism and the Cardinal Series

2020· article· en· W3039734244 on OpenAlexaffabout
Manina Jones

Bibliographic record

VenueForum for Modern Language Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicCrime and Detective Fiction Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsNationalismNational identityColonialismRepresentation (politics)SpectacleHEROSociologyForegroundingDominance (genetics)HistoryAestheticsArtLawLiteraturePolitical science

Abstract

fetched live from OpenAlex

Abstract Giles Blunt’s Cardinal police-procedural novels and their recent television adaptations evidence the noir genre’s sombre aesthetic, focus on a morally tainted hero, are preoccupied with seemingly irrational violence, and fixate on unresolved past injustices. In doing so, they reflect Canada’s aesthetic and ethical relationship to questions of national and transnational culture, colonial territoriality, and the moral principles at stake in the representation of violence. This Canadian ‘re-branding’ of noir features is haunted by deep-seated historical dissension and the present-day repercussions that are at the heart of the country’s national identity. Focusing on the first season of Cardinal (2017) and the novel from which it was adapted, Forty Words for Sorrow (2002), this essay examines the series’ stylish – if conflicted – reworking of noir’s roots in American crime fiction and film, and its use of contemporary Nordic influences, which work to salvage a form of Canadian cultural authenticity from the cultural dominance of US television and film crime dramas.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.052
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0180.015
Scholarly communication0.0080.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.040
GPT teacher head0.277
Teacher spread0.237 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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