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Record W2536880277 · doi:10.1075/lllt.38.11per

Chapter 8. Accounting for variability in L2 data

2013· book-chapter· en· W2536880277 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 learning and language teaching · 2013
Typebook-chapter
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsLinguisticsTask (project management)Modality (human–computer interaction)Object (grammar)ArabicPsychologyCognitionComputer scienceArtificial intelligencePhilosophyEngineering

Abstract

fetched live from OpenAlex

This chapter examines variability in L2 data and isolates two factors that have a significant impact on L2 performance: task modality and linguistic structure. A group of native speakers of Spanish and two groups of intermediate L2 Spanish learners (L1 English and L1 Arabic) completed an oral and a written production task which elicited direct object and oblique Spanish relative clauses. Results indicated that not only did modality have a significant effect on the results, as previously stated (Bialystok 1982; Tarone 1983), arguably because oral and written tasks tap into different types of knowledge (Ellis 2005); but also that linguistic structure was a robust determinant in the speakers’ results, showing that linguistic and cognitive approaches can inform each other.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0610.000

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 head0.024
GPT teacher head0.328
Teacher spread0.304 · 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