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2005· article· en· W2002451058 on OpenAlexaff
Leonardo Recski Ramos

Bibliographic record

VenueTrabalhos em Linguística Aplicada · 2005
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsModal verbVariety (cybernetics)LinguisticsModalSentenceComputer sciencePoint (geometry)CzechPsychologyNatural language processingArtificial intelligenceVerbMathematics

Abstract

fetched live from OpenAlex

This article uses computer learner corpora to compare the variety and frequency of some modal expressions in the writing of university-level EFL students and native speakers. Even though the prime focal point of the investigation is Brazilian EFL writers, the author recurrently relies on comparisons with Spanish and Czech EFL writers in an effort to determine whether certain characteristics of Brazilian EFL writing are likely to stem from mother tongue interference, or are, by and large, shared by EFL writers of different language settings. The study is based on four 33,000-word sub-corpora and takes into account not only modal auxiliary verbs but also a broader variety of modal devices, such as lexical verbs and adverbs with a modal value. Results reveal an overall overuse of modal expressions by all EFL writers, a propensity which may be partially developmental, and partially interlingual. The study also discovers evidence of register interference, where the learners appear to transfer patterns of use from spoken English into their writing, and particularly a high-degree of topic sensitivity in the use of particular modals. It concludes by arguing for the necessity to offer learners a broader variety of modal expressions including larger sentence patterns and modal phrases.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.013

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 head0.013
GPT teacher head0.256
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreMethods

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
Published2005
Admission routes1
Has abstractyes

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