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Record W4407402547 · doi:10.1177/16094069251321250

Absences and Silences in Critical Discourse Analysis: Methodological Reflections

2025· article· en· W4407402547 on OpenAlexaff
Allie Slemon

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCritical discourse analysisSociologyEpistemologyDiscourse analysisPolitical scienceLinguisticsPhilosophyPoliticsLawIdeology

Abstract

fetched live from OpenAlex

Critical discourse analysis (CDA) is a qualitative methodology frequently taken up by researchers to explore complex questions of how discursive power drives inequities. Across approaches to CDA, the emphasis of methodological directions has consistently been centred on examining and illuminating dominant discourses in texts. This paper argues that alongside the analysis of dominant discourses, it is crucial to examine absences and silences that are located within and beyond the text. The exploration of absences and silences can support more fulsome analysis of discourse, and is essential for challenging power structures and illuminating discourses of resistance. In this paper, three analytic strategies for attending to absences and silences in CDA are presented: (i) the lens of the theoretical framework; (ii) interrelation between dominant and excluded discourses; and (iii) positionality and local knowledges. Across each analytic strategy, an exemplar from the author’s research is presented to illustrate the practice of engaging with absences and silences in CDA research. Ultimately, this paper contends that the analysis of absences and silences can support researchers in conducting inquiry that enacts resistance to power structures that perpetuate inequities, and envisioning possible paths toward equity and justice.

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)0.101
metaresearch head score (Gemma)0.090
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.243
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1010.090
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.915
GPT teacher head0.839
Teacher spread0.075 · 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 designTheoretical or conceptual
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

Citations10
Published2025
Admission routes1
Has abstractyes

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