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Record W2165228648 · doi:10.7202/019579ar

L2 Writing and L1 Composition in English: Towards an alignment of effort

2008· article· en· W2165228648 on OpenAlexaffvenue
Beverly Baker

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMainstreamComposition (language)Diversity (politics)WonderComposition studiesSecond language writingLinguisticsInclusion (mineral)PedagogyLinguistic diversitySociologyPsychologyMathematics educationSecond languagePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

In North American university contexts, the language diversity found in English mainstream composition (“L1”) classrooms resembles more and more that found in ESL (“L2”) writing classrooms. As these two groups become less differentiated, those specifically trained in L2 writing might well wonder whether the needs of the non-native speakers of English are acknowledged and addressed in the mainstream classrooms. The author examines several different theoretical constructs that have informed and continue to inform the literature on L1 composition pedagogy, demonstrating that some of these allow for the inclusion of linguistically diverse groups better than others. Fortunately, the recent turn to social and critical approaches to teaching composition reflect well the preoccupations of both L1 and L2 writing teachers. More and more attention is being paid to discussions of “linguistic diversity,” a term which now includes non-native speakers. This suggests a future convergence in the activities of instructors of L1 and L2 writing, leading to benefits for linguistically diverse groups.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.251
GPT teacher head0.393
Teacher spread0.142 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2008
Admission routes2
Has abstractyes

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