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Record W4387079362 · doi:10.1017/9781108766463.027

Sociolinguistic Implications of Orthographic Variation in French

2023· book-chapter· en· W4387079362 on OpenAlexaboutno aff
Sandrine Tailleur

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOrthographySpellingVariation (astronomy)SociolinguisticsLinguisticsLanguage changeSociolinguistics of sign languagesNorm (philosophy)Written languageDiglossiaSociologyNatural languageNeuroscience of multilingualismPolitical scienceReading (process)

Abstract

fetched live from OpenAlex

This chapter discusses selected studies of orthography that focus on the spelling practices by mere users of the language (in crucial opposition to actors from the literate elite – norm makers), concentrating on what they reveal about processes of language change as exemplified by spelling variation. The chapter supports the idea that, within the field of historical sociolinguistics, orthographic variables are now considered a type of linguistic variables. The author shows, on the basis of specific historical sociolinguistic studies, that writers’ variable choices of orthography can inform us about broader mechanisms of language change, but always alongside other types of variation or linguistic information. This chapter examines almost exclusively material from the French language, with the studies under consideration addressing either regional French in France or different varieties of French in Canada. The author situates French orthographic variables within the broader language evolution context, explicating what information spelling variation discloses about the writer’s attitudes toward the (written or spoken) norm, toward the written form, and toward the writer’s linguistic community as a whole. The author also considers how spelling variation compares to other types of language variation in order to contribute to a greater understanding of language change.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.992
Threshold uncertainty score0.824

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)0.0000.000

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 head0.038
GPT teacher head0.211
Teacher spread0.173 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicHistorical Linguistics and Language StudiesFrench-language works237,207