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Record W2311583756 · doi:10.1075/lia.6.2.04vei

Orthographic bias in L3 lexical knowledge

2015· article· en· W2311583756 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.001

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 head0.074
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