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Record W2734865815 · doi:10.5539/ijel.v7n4p197

On Text Interpretation, Summary Production, and Pragmatic Fatigue in EAP Written Discourse

2017· article· en· W2734865815 on OpenAlexvenueno aff
Aisha M. Alhussain

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInterpretation (philosophy)Task (project management)Superordinate goalsComprehensionLinguisticsSummative assessmentClass (philosophy)Computer scienceReading comprehensionProduction (economics)Formative assessmentPsychologyStatement (logic)Reading (process)Representation (politics)Mathematics educationArtificial intelligenceSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

The intent of this interdiscursive endeavor is to argue that EAP text interpretation (reading) and production (writing) can be effectively integrated through the task of summary writing (SW), which academically-oriented students must be able to perform. A discourse-level error analysis enabled us to focus on various features of the summaries which, taken together, indicate how well the (SW) class students had mastered both the goals of text interpretation and the conventions of summary writing, the identification of superordinate and subordinate points, the inclusion of an opening statement of the gist of the primary text, explicit reference to the primary author and text, and successful paraphrasing and representation of substantial claims without pragmatic failure or cross-cultural fatigue. It is a pragmatic feasibility to teach EAP students to be discourse analysts to the extent required by this discourse relevant task, and by learning to do such analysis, they can, in psycholinguistic coinage, integrate formative techniques of comprehension by production and summative techniques of production by comprehension.

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)0.000
metaresearch head score (Gemma)0.099
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.099
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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