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Record W2776386282 · doi:10.5539/ijel.v8n2p115

Insights into CDA: Socio-cognitive Cultural Approach

2017· article· en· W2776386282 on OpenAlexvenueno aff
Nidaa Hussain Fahmi Al-Khazraji

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)Indeterminacy (philosophy)Coherence (philosophical gambling strategy)Interpretation (philosophy)Computer scienceEpistemologyFunction (biology)Discourse analysisLinguisticsArtificial intelligenceMathematicsPhilosophy

Abstract

fetched live from OpenAlex

The overall purpose of the study is to make visible various aspects of CDA. It presents various approaches to discourse analysis and critical discourse analysis to justify the adoption of certain models over others. A general theoretical account of the various influential approaches to the text will be presented first, followed by a critical approach next to arrive at their range of usefulness as a means to an end. Besides the absence of a general terminological consensus among text linguists, the fact is that there is no one generally accepted theory of discourse analysis that undertakes to provide the complete analysis of texts. While all text analysts acknowledge the fact that a text has structure, coherence, function, organisation, character and development, their approaches differ as to how each of these properties is realised and mutually related to other properties, hence the advantages of the eclectic approach which provides for the necessary step of integrating a variety of compatible systems of discourse analysis whenever these are found useful and adaptable to the requirements of each study. Such an approach, while lessening the problems of indeterminacy and partiality, remains just one model yielding one specific interpretation. However, variation in interpretations is resorvable and can ultimately be made definitive given a text and the same vital background information and approache(s).

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)0.055
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.034
GPT teacher head0.320
Teacher spread0.285 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicDiscourse Analysis in Language StudiesFrench-language works237,207