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Record W7057993157

Le corpus Ebullition : un outil pour l’analyse de la representation de l’espace et du paysage linguistique dans la BD quebecoise

2023· article· en· W7057993157 on OpenAlexaboutno aff

Bibliographic record

VenueCINECA IRIS Institutial Research Information System (University of Genoa) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)Corpus linguisticsLinguistic analysisSpace (punctuation)Face (sociological concept)ComicsText corpusFrench
DOInot available

Abstract

fetched live from OpenAlex

This article presents the possible uses of the Ebullition corpus for research into the representation of space in Quebecois comics. By encoding the texts of the comics in the Ebullition corpus in XML-TEI language, the Ebullition corpus can be used for advanced searches that highlight linguistic elements relating to space: not only odonyms, but also any text that forms part of the linguistic landscape (place names, business names, posters, signs, etc.). This research will reveal, for example, the face of Montreal at a given time, or the relationship between French and English as represented in Quebecois comics.

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)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.261
Threshold uncertainty score0.526

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0130.015
Science and technology studies0.0040.003
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.003

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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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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