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Record W2311583756 · doi:10.1075/lia.6.2.04vei

Orthographic bias in L3 lexical knowledge

2015· article· en· W2311583756 on OpenAlexaff
Outi Veivo, Eija Suomela-Salmi, Juhani Järvikivi

Bibliographic record

VenueLanguage Interaction and Acquisition · 2015
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLinguisticsOrthographic projectionMeaning (existential)PsychologySimilarity (geometry)Natural language processingComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

In this paper, we examine some of the factors that might influence the accessing of meanings of written and spoken L3 words. We tested learners of L3 French who had Finnish as their L1 and were highly competent in L2 English. They were presented with L3 French words in written and spoken form, and were asked to give a possible translation for the target word in L1 and to rate their level of confidence in the meaning given. Because of their instructional learning background, we expected orthographic forms to be more familiar than phonological ones. This hypothesis was confirmed. The meanings of the L3 words presented were accessed more easily and more accurately in the orthographic than in the phonological modality, although this asymmetry decreased with a higher level of proficiency. The confidence ratings were negatively affected by a similarity to L2 words. General implications for L3 lexical knowledge are discussed.

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)0.016
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.393
Teacher spread0.319 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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Same venueLanguage Interaction and AcquisitionSame topicSecond Language Acquisition and LearningFrench-language works237,207