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Record W3092205386 · doi:10.5539/ijel.v10n6p322

Appraisal and Party Positioning in Parliamentary Debates: A Usage-Based Critical Discourse Analysis

2020· article· en· W3092205386 on OpenAlexvenueno aff
Anissa Berracheche

Bibliographic record

VenueInternational Journal of English Linguistics · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOperationalizationAffect (linguistics)PoliticsRefugeeCritical discourse analysisSociologyCritical appraisalSocial psychologyPolitical sciencePsychologyPublic relationsEpistemologyLawIdeologyPhilosophyMedicine

Abstract

fetched live from OpenAlex

This article presents a corpus-driven study of evaluative discourses surrounding asylum seekers in parliamentary debates. It explores how Australian political parties have expressed unfavorable attitudes toward asylum seekers. These attitudes are operationalized by implementing Martin and White’s appraisal framework, which comprises affectual (affect), ethical (judgment), and aesthetic (appreciation) values. The findings reveal that the subcategories of affect, judgment, and appreciation are strategically deployed by both right- and left-wing parties. The right-wing discourse, conveying ethical values, emphasizes the difference between “in” and “out” groups, whereas the left-wing discourse, engaged in affectual values, demonstrates their humanitarian side. The study has also a methodological focus, namely, testing the feasibility of the behavioral profile approach in critical discourse analysis to obtain more replicable and reliable quantitative results. The method consists of the manual annotation of the corpus and multivariate statistical analysis.

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)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.032
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.331
Teacher spread0.305 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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