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Record W4386605939 · doi:10.1080/24751839.2023.2254956

The formalization of interlanguage: the example of object clitic pronouns acquisition in French L2

2023· article· en· W4386605939 on OpenAlexafffund
Amina Affes, Ismaïl Biskri, Adel Jebali

Bibliographic record

VenueJournal of Information and Telecommunication · 2023
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversité du Québec à Trois-RivièresConcordia University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsInterlanguageCliticLinguisticsComputer scienceGrammarObject (grammar)Context (archaeology)Second-language acquisitionNatural language processingArtificial intelligencePsychologyHistory

Abstract

fetched live from OpenAlex

The interlanguage of second and foreign language French learners presents certain patterns that offer a formal framework within the context of the Applicative Combinatory Categorical Grammar. These patterns manifest in the acquisition of object clitics which presents considerable difficulties for French second language learners. To address this, we thoroughly analysed the object clitic pronouns that appeared in a corpus of texts by examining the different facets of these pronouns. A formal analysis was then proposed. The objective of this research is to establish an automated system for processing learners’ interlanguage in terms of the ACCG.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.270
Teacher spread0.259 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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Same venueJournal of Information and TelecommunicationSame topicNatural Language Processing TechniquesFrench-language works237,207