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Record W4221036202 · doi:10.18192/olbij.v11i1.6180

Academic writing re-designed: Connecting languages and literacy in the assemblage of EAP

2022· article· en· W4221036202 on OpenAlexaffvenue
Eugenia Vasilopoulos

Bibliographic record

VenueOLBI Journal · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAssemblage (archaeology)Meaning (existential)SociologyDeleuze and GuattariAffect (linguistics)PedagogyLiteracyMeaning-makingQualitative researchLinguisticsPsychologySocial scienceCommunicationArchaeologyGeography

Abstract

fetched live from OpenAlex

This study draws on the combined perspectives of “A pedagogy of multiliteracies” (New London Group, 1996) and assemblage and affect (Deleuze & Guattari, 1980/1987) to examine how neoliberal identities shape how English for academic purposes (EAP) students compose a source-based research paper. Such exploration is necessary to account for the range of influences that contribute to students’ meaning making and textual production, especially when academic dishonesty is involved. Interview data from one atypical student participant is presented and analyzed through the post-qualitative method of rhizoanalysis to highlight how (mis)intended meaning in the design process can be (mis)interpreted. Analysis from a pedagogy of multiliteracies framework combined with assemblage and affect reveal the unsuspecting neoliberal influence that shape learning experiences in EAP. Based on these findings, critical implications for EAP pedagogy and research are proposed to address international students’ lived realities as digital-transnational citizens.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.012
Scholarly communication0.0110.008
Open science0.0010.011
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.317
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes2
Has abstractyes

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