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Record W2981073106 · doi:10.3917/her.174.0225

Cartographier les migrations dans un grand quotidien

2019· article· fr· W2981073106 on OpenAlexaff
Mathilde Costil, Francesca Fattori

Bibliographic record

VenueHérodote · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsWorld Federation of Science Journalists
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Depuis l’apparition, dans les médias et notamment dans ses colonnes, de l’expression « crise de migrants », le quotidien Le Monde a traité de façon extensive ce phénomène complexe, sous forme de reportages, d’analyses, de débats, mais aussi de cartes. Représentation graphique synthétique de phénomènes géographiques complexes, la carte est un outil incontournable pour appréhender la réalité mouvante des migrations, dans plusieurs régions et à plusieurs échelles. C’est aussi, dans la pratique du service infographie du journal Le Monde , un produit éditorial qui propose plusieurs niveaux de lecture, résultant du choix entre données statistiques, analyse qualitative et récits sensibles.

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)0.004
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 score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0050.003
Scholarly communication0.0040.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.024
GPT teacher head0.235
Teacher spread0.211 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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