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Record W4391282588 · doi:10.5539/elt.v17n2p25

How Causal Relation Affects the Construction of Problem-Solution in Argumentation Essays

2024· article· en· W4391282588 on OpenAlexvenueno aff
Nobuko Tahara

Bibliographic record

VenueEnglish Language Teaching · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsArgumentation theoryPsychologyRelation (database)EpistemologyLinguisticsMathematics educationCognitive psychologySocial psychologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

The Problem-Solution text pattern is often used in academic writing. The present study investigates how Problem-Solution is used in non-native speaker (NNS) argumentation essays by Japanese speaking students, in comparison with native speaker (NS) essays by American students. By taking the clause relational approach, the present study attempts to find how the students use causal relation when employing the Problem-Solution pattern in their essays. The investigation focuses on problem, which is a causative device, as well as a shell noun that can construct the text. This paper will show that in the two corpora the students’ use of causal relation was similar in frequency and drew on the same types of causal categories; however, how the causal categories were expressed in lexico-grammatical patterns was often different. Furthermore, NNS students used problem in non-causal relation significantly more frequently than NS students did. This paper points to what lacked in NNS essays in order for the students to use causal relations in the same ways as NS students did in their essays (e.g., type of verbs, rheme-theme development). A discussion of the pedagogical implications of the findings provides insights which could be helpful to educators developing syllabi and teaching academic writing to NNS students.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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