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Record W2505807112 · doi:10.1075/mdm.1.11lud

Traces of monolingual and plurilingual ideologies in the history of language policies in France

2012· book-chapter· en· W2505807112 on OpenAlexaboutno aff
Georges Lüdi

Bibliographic record

VenueMultilingualism and diversity management · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLinguistic and Sociocultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyDoxaNormativeFrenchLinguisticsStandard languageLanguage ideologyRank (graph theory)Political scienceSociologyPoliticsLawPhilosophyEpistemologyMathematics

Abstract

fetched live from OpenAlex

French is often quoted as the forerunner and model of a very normative and top-down managed language, following the language policy of an archetypal monolingual nation-state, be it France, Quebec or other French-speaking communities in the world. This particular contribution is not going to prove the contrary. However, we will try to show that even the French language and the French-speaking nations are not as much of a monolithic block as they are frequently perceived to be. At different moments in history other ideologies on the French language appeared. They concerned, on the one hand, the relationship between “French” and other languages – historical minorities and immigrant languages – and, on the other hand, the attitudes towards different varieties of French. In other words, the history of French must take into account three different elements: (a) the elaboration, over the centuries, of the endoxa, that is the official ideology, fixed in the dominant discourse; (b) the existence and, at some moments in history, prioritization of other types of discourse, manifesting more or less opposite opinions; (c) the fact that different beliefs may co-exist, that contradictory voices can be heard simultaneously at certain moments and also struggle in the arena of public discourse, enabling the (en)doxa to be polyphonic.

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)0.001
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 score0.213
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.043
GPT teacher head0.287
Teacher spread0.244 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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