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Record W2313090973 · doi:10.1515/auseur-2015-0010

State Traditions and Language Regimes: A Historical Institutionalism Approach to Language Policy

2015· article· en· W2313090973 on OpenAlexaffabout
Selma K. Sonntag, Linda Cardinal

Bibliographic record

VenueActa Universitatis Sapientiae European and Regional Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLanguage policyNormativeHistorical institutionalismSociologyPhilosophy of languageEpistemologyContext (archaeology)PoliticsSociolinguisticsLinguisticsLanguage changeState (computer science)AutonomyInstitutionalismPositive economicsSocial sciencePolitical scienceComputer scienceLawEconomicsPhilosophyHistory

Abstract

fetched live from OpenAlex

Abstract This paper is an elaboration of a theoretical framework we developed in the introductory chapter of our co-edited volume, State Traditions and Language Regimes (McGill-Queen’s University Press, 2015). Using a historical institutionalism approach derived from political science, we argue that language policies need to be understood in terms of their historical and institutional context. The concept of ‘state tradition’ focuses our attention on the relative autonomy of the state in terms of its normative and institutional traditions that lead to particular path dependencies of language policy choices, subject to change at critical junctures. ‘Language regime’ is the conceptual link between state traditions and language policy choices: it allows us to analytically conceptualize how and why these choices are made and how and why they change. We suggest that our framework offers a more robust analysis of language politics than other approaches found in sociolinguistics and normative theory. It also challenges political science to become more engaged with scholarly debate on language policy and linguistic diversity.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.475

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.124
GPT teacher head0.385
Teacher spread0.262 · 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 teacher head, 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

Citations35
Published2015
Admission routes2
Has abstractyes

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