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Record W4256034075 · doi:10.1353/cml.2005.0047

The Use of L1 in an L2 On-Line Chat Activity

2005· article· en· W4256034075 on OpenAlexvenueno aff
Joshua J. Thoms, Jianling Liao, Anja Szustak

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2005
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsnot available
Fundersnot available
KeywordsLine (geometry)LinguisticsPsychologyComputer scienceCommunicationNatural language processingMathematicsPhilosophy

Abstract

fetched live from OpenAlex

This study focuses on the use of the native language (L1) by second language (L2) learners when carrying out a collaborative jigsaw task in a computer chat environment. It investigates the extent and function of L1 use by means of a sociocultural theoretical framework. The research project was carried out in three languages: Chinese, German, and Spanish. Students were assigned to dyads at random and were asked to perform a jigsaw task activity. The chat logs were collected and analyzed via descriptive statistics and discourse analysis. The findings suggest that across all three languages, the students used their L1 (English) to varying degrees and for a variety of functions. ‘Moving the task along’ (Swain & Lapkin, 2000a) was the primary function of the L1. Further examination of the chat logs indicates that several factors affected the use of L1, such as participants' task management strategies and the use of symbols.

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)0.005
metaresearch head score (Gemma)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.058
GPT teacher head0.277
Teacher spread0.219 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Modern Language Review/ La Revue canadienne des langues vivantesSame topicDigital Communication and LanguageFrench-language works237,207