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Record W4310532355 · doi:10.5539/ijel.v13n1p43

The Second-Language Essay as Cognitive Task: Complexity, Subjectivity and Emotion

2022· article· en· W4310532355 on OpenAlexvenueno aff
CA DeCoursey

Bibliographic record

VenueInternational Journal of English Linguistics · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTask (project management)SubjectivityCognitionSentenceFrame (networking)PsychologyStress (linguistics)Focus (optics)Face (sociological concept)Resource (disambiguation)LinguisticsCognitive psychologyComputer scienceNatural language processingEpistemology

Abstract

fetched live from OpenAlex

Models of task complexity indicate the multiple processes and challenges second-language university students face when learning to write an essay. Studies of complex cognitive tasks frame emotion as an aspect of individual differences. This study used Appraisal analysis to assess subjective attitudes realised across four weeks of writing an essay, content analysis to identify how students took up instructor input, and co-frequency to identify strong connections. Results indicate that students focus on researching essay content at the expense of structure and language. They find topic sentences more difficult than thesis statements, and have difficulty collating sentence-level proficiency with the sophisticated discourse-level demands of the essay task. Content and attitude frequencies suggested that relatively little work was done in the first week, substantial work was done in weeks two and three, and realisations dropped sharply in the final week, suggesting resource-dispersing impacts in the final stretch. Results highlight the need for somatic measures of task complexity and effort, due to frequent realisations of stress and stress relief, and their co-frequency with misery.

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)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, 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: none
Teacher disagreement score0.777
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.296
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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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