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Record W1989860540 · doi:10.3138/cmlr.1753

Academic Language Socialization in High School Writing Conferences

2014· article· en· W1989860540 on OpenAlexvenueno aff
Betsy Gilliland

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSocializationConstructiveLiteracyAcademic writingPedagogyPsychologyMathematics educationSociologyComputer scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract: This study examines multilingual high school writers’ individual talk with their teachers in two advanced English language development classes to observe how such talk shapes linguistically diverse adolescents’ writing. Addressing adolescent writers’ language socialization through microethnographic discourse analysis, the author argues that teachers’ oral responses during writing conferences can either scaffold or deter students’ socialization into valued ways of using academic language for school writing. She suggests what forms of oral response provide scaffolding and what forms might limit multilingual adolescent learners’ academic literacy. Constructive interactions engaged students in dialogue about their writing, and students included content or phrasing from the interaction in their texts. Unhelpful interactions failed to foster students’ language development in observable ways. Although teachers attempted to scaffold ideas and language, they often did not guide students’ discovery of appropriate forms or points. These interactions represent restrictive academic language socialization: while some students did create academic texts, they learned little about academic language use.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.249
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 designNot applicable
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

Citations22
Published2014
Admission routes1
Has abstractyes

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