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Record W2170069310 · doi:10.1093/sp/jxq019

An Exploration of Redneck Whiteness in Multicultural Canada

2010· article· en· W2170069310 on OpenAlexaboutno aff
Amy E. O’Connell

Bibliographic record

VenueSocial Politics International Studies in Gender State & Society · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismRuralityGender studiesSociologyNarrativeFrontierIndigenousPoliticsWhite (mutation)ColonialismPolitical scienceRural areaLawArt

Abstract

fetched live from OpenAlex

As Canada celebrates forty years of official multiculturalism (1971), a shifting urban/rural dyad (Neal) is central to its configuration. Its urban centers are positioned as diverse racialized spaces unlike their less diverse and more white rural counterparts. In this paper, I explore the relationships between rurality, whiteness, and multiculturalism through the rise of the redneck in North America and the Canadian Redneck Games in rural Ontario. While seemingly politically incorrect, I argue that these expressions of rural whiteness provide both critique and coherence to liberal whiteness and multicultural policies. Celebrations of rural whiteness uphold the frontier narratives (Furniss), the past which helps structure multiculturalism as a contemporary form of white liberal tolerance of and benevolence toward Indigenous peoples and racialized others. These mutually sustaining narratives constitute a contemporary politics which is unable to address past colonial crimes and contemporary racial violence.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.727

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0430.013
Scholarly communication0.0100.003
Open science0.0020.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.000

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 head0.076
GPT teacher head0.332
Teacher spread0.256 · 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 designQualitative
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

Citations43
Published2010
Admission routes1
Has abstractyes

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