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‘<i>Ch'us mon propre Bescherelle</i>’: Challenges from the Hip‐Hop nation to the Quebec nation<sup>1</sup>

2009· article· en· W2053490780 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Sociolinguistics · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic and Sociocultural Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsFrenchLyricsSociologyLingua francaPopulationLinguistic diversityLanguage ideologyPrestigeLinguisticsMedia studiesPolitical sciencePoliticsIdeologyLiteratureArt

Abstract

fetched live from OpenAlex

We examine the uses of and attitudes towards language of members of the Montreal Hip‐Hop community in relation to Quebec language‐in‐education policies. These policies, implemented in the 1970s, have ensured that French has become the common public language of an ethnically diverse young adult population in Montreal. We argue, using Blommaert's (2005) model of orders of indexicality, that the dominant language hierarchy orders established by government policy have been both flattened and reordered by members of the Montreal Hip‐Hop community, whose multilingual lyrics insist: (1) that while French is the lingua franca , it is a much more inclusive category which includes ‘Bad French,’ regional and class dialects, and European French; and (2) that all languages spoken by community members are valuable as linguistic resources for creativity and communication with multiple audiences. We draw from a database which includes interviews with and lyrics from rappers of Haitian, Latin‐American, African‐American and Québécois origin.

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.

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)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.310
Teacher spread0.255 · 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