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Record W1574226729 · doi:10.18552/joaw.v5i1.137

An International Discourse Community, an Internationalist Perspective: Reading EATAW Conference Programs, 2001-2011

2015· article· en· W1574226729 on OpenAlexaff
Judith Kearns, Brian A. Turner

Bibliographic record

VenueJournal of Academic Writing · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsPresentation (obstetrics)Perspective (graphical)Reading (process)Context (archaeology)Composition (language)SociologyPedagogyMedia studiesLinguisticsPolitical scienceHistoryLawComputer science

Abstract

fetched live from OpenAlex

This article seeks to characterize the discourse community represented by the biennial conferences of the European Association for the Teaching of Academic Writing (EATAW). Drawing on information from EATAW's conference programs, the authors define the topical emphases of the 565 standard presentation abstracts (SPAs) accepted for the first six conferences, identify some of the community's dominant research practices and common methods of presentation, and track the changing international distribution of presenters over time. We conclude that the EATAW discourse community, true to its name, has remained focused primarily on pedagogy and on pragmatic research aimed at improving teaching practices. Working in a multilingual context, EATAW teachers/researchers tend towards an 'internationalist perspective' (Horner and Trimbur 2002: 624), one that is attentive to linguistic and cultural differences and favours empirical research as a means of identifying diverse student needs. This perspective, along with a tendency toward cross-institutional and international research partnerships, stands in contrast to the perspective of the Conference on College Composition and Communication (CCCC) the conference which best represents the American composition tradition.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.137
GPT teacher head0.411
Teacher spread0.274 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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