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Record W2076499139 · doi:10.3828/ejlp.2013.4

A "Bill 101" in Switzerland? Language planning in the canton of Jura

2013· article· en· W2076499139 on OpenAlexaboutno aff
Sara Cotelli Kureth

Bibliographic record

VenueEuropean Journal of Language Policy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutionParliamentIdeologyState (computer science)LawSociologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Although multilingual, Switzerland has rarely been seen as a state with a strong tradition of language planning; quite the opposite. This article will focus on one region of French-speaking Switzerland, the canton of Jura, a region formerly attached to the canton of Bern and which became the 23rd Swiss canton in 1979. In the 1960s and 1970s, the Jura autonomist movement based part of its argumentation for the creation of a new state within the Swiss confederation on language ideologies. It is of great interest to note that, once the new canton had been constituted, questions of language planning were raised, mostly by actors who had been influential in the autonomist movement. First, the writing of the canton's new constitution gave rise to a series of questions about language. Jura's constitution is the first constitution of a monolingual canton dealing with language issues. Moreover, a motion was passed at the Parliament in 1985 and is the base for a new law about the "use of the French language" that has recently been accepted by the Parliament. Why is the canton of Jura the sole monolingual Swiss canton with such a language law? Why is the 1985 project close in its content to the famous Bill 101 from Quebec? Why is it felt that French is threatened in the Jura and needs a law to help protect and illustrate it? Analysing the situation from a critical sociolinguistics perspective, this article will focus on the language ideologies put forward in these laws (the constitution, the draft law from the 1980s and the new law written in the twenty-first century) and the power issues at work in this context.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.014
Scholarly communication0.0080.002
Open science0.0010.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.339
Teacher spread0.315 · 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 designObservational
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

Citations2
Published2013
Admission routes1
Has abstractyes

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