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Record W4387333009 · doi:10.1075/task.22009.bar

Exploring task effects on register variation in second language learners’ writing

2023· article· en· W4387333009 on OpenAlexaff
Khaled Barkaoui

Bibliographic record

VenueTASK Journal on Task-Based Language Teaching and Learning · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsYork University
Fundersnot available
KeywordsFormalityRegister (sociolinguistics)FluencyVariation (astronomy)Task (project management)Computer scienceLinguisticsNarrativeSecond languageSecond language writingPsychologyMathematics education

Abstract

fetched live from OpenAlex

Abstract Task-based research often focuses on the main effects of task variables on measures of complexity, accuracy, and fluency of second language (L2) writing performance. This study aimed to extend this line of research by examining the main and interaction effects of task type, learner L2 proficiency, and L2 study on register variation in L2 learners’ writing. Each of 42 Chinese learners of English as a foreign language responded to independent and integrated writing tasks before and after nine months of English language study. Each essay ( N = 168) was rated on level of formality and tagged for various lexico-syntactic features. Overall, integrated essays were judged to use more formal language and were more informationally dense than were the independent essays. More proficient students were judged to use more formal language than did less proficient students. After instruction, students’ writing became more formal, more informationally dense, and more narrative. The findings and their implications for the teaching and assessment of L2 writing 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 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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.266
Teacher spread0.227 · 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 teacher head, not a consensus.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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