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Record W2977670212 · doi:10.3917/ls.168.0119

Une langue « tordue » ? Réappropriations identitaires par les récits de vie : pratiques translangagières et agentivité en contexte migratoire

2019· article· fr· W2977670212 on OpenAlexaff
Emmanuelle Radar, Emmanuelle Le Pichon

Bibliographic record

VenueLangage et société · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicLinguistic and Sociocultural Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

La question des rapports de force entre langues en période de mutation géopolitique est particulièrement saillante lorsque l’individu en situation de migration est amené à se raconter. Nous proposons une analyse à la fois sociolinguistique et postcoloniale d’entretiens, menés aux Pays-Bas, entre des nouveaux arrivants francophones et une interlocutrice néerlandaise, qui met à jour un rôle inédit des répertoires plurilingues et stratégies translangagières dans la médiation des identités par le biais du français. Nous croisons les points de vue historiques et contemporains, pour comprendre comment l’espace translangagier qui se crée dans ce contexte permet la rencontre, la modification des rapports de domination, et la réhabilitation identitaire. Plus généralement, nous interrogeons le rôle de la langue cible comme moyen privilégié d’accession à l’insertion sociale. Nous plaidons pour une reconnaissance et une prise en compte par les institutions des biographies langagières.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.019
Scholarly communication0.0070.006
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.027
GPT teacher head0.340
Teacher spread0.313 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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