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Language and the nation‐state: Challenges to sociolinguistic theory and practice<sup>1</sup>

2008· article· en· W1731996347 on OpenAlexaffabout
Monica Heller

Bibliographic record

VenueJournal of Sociolinguistics · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSociolinguisticsSociologyTransnationalismSocial identity theoryGlobalizationLinguisticsSocial orderEpistemologyGender studiesSocial groupSocial sciencePolitical sciencePolitics

Abstract

fetched live from OpenAlex

Communities, identities, processes, and practices are key linked concepts of concern to research on the role of language in the construction of social relations within the nation‐state. In the current globalizing context, sociolinguistics has begun to recognize the need to reorient studies of language, community, and identity in the nation‐state away from autonomous structure and towards process and practice, in order to capture the ways in which linguistic variation is central to new forms of social organization. Such an approach examines the circulation of communicative, symbolic, and material resources, as well as the trajectories of social actors and of discursive spaces. The example of francophone Canada shows how dominant ideas about language as bounded systems, identities as stable social positions, and communities as uniform social formations are superseded by mobility and multiplicity. Sociolinguistics is well positioned to take on the challenge of addressing how social actors construct such flows and transformations and to contribute to a social theory of globalization, transnationalism, and the new economy.

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)0.017
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0170.112
Scholarly communication0.0290.024
Open science0.0040.010
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.084
GPT teacher head0.444
Teacher spread0.361 · 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 designTheoretical or conceptual
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

Citations167
Published2008
Admission routes2
Has abstractyes

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