MétaCan
Menu
Back to cohort
Record W2755000596 · doi:10.1558/lst.26454

Exploring the social nature of L2 writing

2017· article· en· W2755000596 on OpenAlexaff
Subrata Bhowmik

Bibliographic record

VenueLanguage and Sociocultural Theory · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAffordanceAcademic writingTask (project management)PsychologyComposition (language)Writing processContext (archaeology)Second language writingMediationClass (philosophy)Division of labourEnglish for academic purposesProcess (computing)Basic writingPedagogyMathematics educationSociologyHigher educationLinguisticsComputer scienceSecond languageCognitive psychologyHistoryPolitical scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

Using an activity system framework this study examined the social nature of the writing processes by investigating the division of labor in the writing processes of 31 ESL learners. The study involved one of the four major writing assignments in a required first-year composition course for ESL students at a North American university. Data was collected from: (a) A semi-structured interview with each participant, (b) process logs kept by all participants, (c) classroom observation notes, and (d) class materials. Findings indicate that L2 writers used various context-specific affordances derived from division of labor to accomplish their writing task. The study arrived at these findings by creating taxonomies of people who were part of their writing process and examining the influence that these people had on their writing. The findings show that people related to L2 writers within the social space in both academic and non-academic capacities mediate them in non-trivial ways. This mediation renders not only positive but also negative effects on the production of L2 texts. Implications for L2 writing pedagogy and research 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.999

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.302
Teacher spread0.237 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueLanguage and Sociocultural TheorySame topicEFL/ESL Teaching and LearningFrench-language works237,207