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Record W3004740225 · doi:10.4000/books.pup.50265

Mobilités dans l’espace migratoire Algérie France Canada

2019· book· fr· W3004740225 on OpenAlexaboutno aff
Nathalie Thamin, Mohammed Zakaria Ali-Bencherif, Anne-Sophie Calinon

Bibliographic record

VenuePresses universitaires de Provence eBooks · 2019
Typebook
Languagefr
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

Vecteur majeur des dynamiques sociales du monde contemporain, la mobilité est aujourd’hui l’un des concepts les mieux partagés en sciences humaines et sociales. Cet ouvrage thématise la mobilité, ainsi que d’autres concepts liés, comme la circulation, la globalisation, le projet migratoire, la mobilité étudiante, en les éclairant du point de vue géographique, sociologique, sociolinguistique et sémiologique. On se focalise sur les mouvements migratoires observés dans l’espace francophone (Maghreb, France, Canada), en particulier au départ de l’Algérie. Quels outils méthodologiques et théoriques permettent de caractériser les mobilités contemporaines ? Quels sont les apports des sciences humaines et sociales dans la manière de les appréhender ? De quelle manière les mobilités langagières sont-elles imbriquées aux mobilités géographiques et sociales ? Dès lors, quelles dynamiques et reconfigurations identitaires observe-t-on chez l’étudiant en mobilité ? L’ouvrage s’inscrit dans le cadre du programme scientifique CEM, « Dynamiques spatiales, langagières, identitaires, de la circulation migratoire étudiante » (Maghreb, France et Canada) porté par des chercheures en sciences du langage Anne-Sophie Calinon, Nathalie Thamin (université Bourgogne Franche-Comté) et Katja Ploog (université d’Orléans) en collaboration avec Mohammed Zakaria Ali-Bencherif et Azzeddine Mahieddine (université de Tlemcen, Algérie).

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.001
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 head0.032
GPT teacher head0.282
Teacher spread0.249 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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