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Record W2494353997 · doi:10.1075/jlp.15.1.01ves

Language ideologies in social media

2016· article· en· W2494353997 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Language and Politics · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologySocial mediaLinguisticsLanguage ideologyComplaintMedia studiesSociologyBacklashNegativity effectPolitical sciencePsychologyLawPoliticsSocial psychologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

When inspectors from the Office québécois de la langue francçaise (OQLF) objected to the use of the word “pasta” in a Montreal restaurant in February 2013, a backlash in news and social media erupted internationally. Ensuing pressure led to the resignation of the OQLF head and a revision of OQLF language complaint procedures; the Pastagate story also contributed to mounting negativity towards the province and its language. Social media have been credited with playing a role in the proliferation of the story and its impact. Drawing on a corpus of Tweets containing PASTAGATE, this paper uses corpus-assisted discourse studies to explore language ideologies in English and French Tweets. Findings reveal divergent language ideologies and representations of the Pastagate affair. The paper concludes by suggesting that language ideological debates in a superdiverse online world may have implications for minority languages in the offline world of nation-states.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.258
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.297
Teacher spread0.271 · 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