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Record W2094374500 · doi:10.1177/0075424209340313

Hip-hop in a Post-insular Community

2009· article· en· W2094374500 on OpenAlexaffabout
Sandra Clarke, Philip Hiscock

Bibliographic record

VenueJournal of English Linguistics · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVernacularSociologyIdeologySociolinguisticsLinguisticsGender studiesWhite (mutation)Focus (optics)Unit (ring theory)HistoryPoliticsPolitical sciencePsychologyLawPhilosophy

Abstract

fetched live from OpenAlex

The focus of this article is Gazeebow Unit, an adolescent hip-hop group from Newfoundland, Canada, whose tracks, which date from 2005, are available only online. As white rappers whose language is grounded in vernacular Newfoundland English, their rap raises obvious questions relating to both authenticity and hybridization. Despite the group’s use of local linguistic and semiotic resources to style young working-class Newfoundland male “skeet” identity, their authenticity as both working-class Newfoundlanders and rappers was soon to be publicly contested. Though local language and dialect typically represent “resistance vernaculars” in global hip-hop, the use of vernacular Newfoundland English as a performance register on the part of Gazeebow Unit is shown to be considerably more complex. At one level at least, Gazeebow Unit are engaged in parody, or “strategic inauthenticity,” one ramification of which is to reproduce and reinforce dominant ideologies of social class.

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)0.001
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 score0.510
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0270.006
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.027
GPT teacher head0.322
Teacher spread0.295 · 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

Citations23
Published2009
Admission routes2
Has abstractyes

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