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Record W2777581233

A la recherche des langues perdues.

2014· article· fr· W2777581233 on OpenAlexaboutno aff
Sica Acapo, Laura Mareglia, Valeria Kolbe, Daniel Negers, Guillaume Malingri de Bagnolo, Iryna Dmytrychyn, Frosa Pejoska‐Bouchereau, Jean de Dieu Karangwa, Camille Gerschel Hautefeuille, Peter Stockinger, Pauline Massol

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2014
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

La reputation de l’INALCO s’est construite sur l’eventail monumental de langues que l’institut propose. Des 93 langues enseignees, seules quelques-unes (japonais, chinois, coreen, arabe, russe) prennent le devant de la scene de par le nombre de leurs etudiants et la multitude d’evenements qui leur sont consacres. Qu’en est-il donc du peul, du wolof, du telougou ou du macedonien ? Ce petit documentaire tente de mettre en lumiere ces langues rares a petits effectifs. La preservation de ces langues est un enjeu important : l’INALCO serait le dernier etablissement au monde a proposer l’enseignement du maya ; une enseignante d’inuktitut de l’INALCO se revele etre une aide precieuse pour des travailleurs canadiens, a qui elle enseigne a distance afin de leur permettre de dialoguer avec les populations Inuit locales. « A la recherche des langues perdues » propose donc de decouvrir ces langues rares en partant a la rencontre des enseignants et des etudiants. Alors que les derniers revelent leurs raisons pour etudier ces langues, leurs motivations, et leur quotidien ; les enseignants egalement reflechissent a la place de la langue qu'ils enseignent ainsi que les enjeux qu'il y a a etudier des langues etrangeres, parfois aussi petites que les leurs.

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)0.008
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.277
Teacher spread0.195 · 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.

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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueHAL (Le Centre pour la Communication Scientifique Directe)Same topicHistorical Linguistics and Language StudiesFrench-language works237,207