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Record W3187815040 · doi:10.14288/bctj.v6i1.390

Thriving through Uncertainties: The Agency and Resourcefulness of First-Year Chinese English as an Additional Language Writers in a Canadian University

2020· article· en· W3187815040 on OpenAlexaffabout
Jing Mao

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsThrivingAgency (philosophy)MainstreamSocializationPedagogyPsychologyConversationSociologySocial psychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Amidst the increased enrolment of international Chinese English as an additional language (EAL) students in North American universities, scholars have reported on their academic and social experiences in navigating English-medium studies (e.g., Liu, 2016; Zhang & Zhou, 2010). Although informative, some studies focus on EAL learners’ perceived deficient language proficiencies, and how these deficits can negatively impact their academic success. In contrast to studies based on deficit models, this study argues that participants exhibit agency as evidenced in their responses to challenges encountered in and changes to their perceptions of and practices in academic writing. Employing an ecological perspective and (second) language socialization theories (Duff, 2010, 2019; van Lier, 2004, 2008), this qualitative case study examined how six first-year Chinese EAL learners enacted their agency and resourcefulness when navigating their academic writing trajectories. Ultimately, this study’s findings recommend that composition faculty, administrators, and EAL educators recognize EAL writers’ agency in accessing multiple resources while acknowledging their writing challenges, providing an optimal learning environment, and empowering them to thrive in their mainstream composition studies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0570.000

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 head0.115
GPT teacher head0.420
Teacher spread0.305 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

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