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Record W2774524497 · doi:10.5539/elt.v11n1p110

Quantitative Research in Systemic Functional Linguistics

2017· article· en· W2774524497 on OpenAlexvenueno aff
Qingshun He

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
FundersDepartment of Education of Guangdong Province
KeywordsSystemic functional linguisticsApplied linguisticsLinguisticsQuantitative linguisticsPsychologyCompetence (human resources)Clinical linguisticsTheoretical linguisticsComputational linguisticsStructural linguisticsQualitative researchLanguage assessmentComputer scienceSociologyPhilosophySocial scienceSocial psychology

Abstract

fetched live from OpenAlex

The research of Systemic Functional Linguistics has been quite in-depth in both theory and practice. However, many linguists hold that Systemic Functional Linguistics has no hypothesis testing or experiments and its research is only qualitative. Analyses of the corpus, intelligent computing and language evolution on the ideological background of Systemic Functional Linguistics show that this theory focuses its research on language-in-use and is significantly quantitative in nature. It carries out both top-down and bottom-up approaches in specific studies and emphasizes on the combination of quantitative and qualitative research methods, the complementation of competence and performance data and the integration of manual and automatic operations.

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)0.004
metaresearch head score (Gemma)0.017
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.404
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.067
GPT teacher head0.393
Teacher spread0.326 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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