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Record W3168085888 · doi:10.1177/16094069211020903

Relational Critical Discourse Analysis: A Methodology to Challenge Researcher Assumptions

2021· article· en· W3168085888 on OpenAlex
Dorothy Vaandering, Kristin Reimer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSociologyHarmCritical discourse analysisSpace (punctuation)EpistemologyDiscourse analysisResearch methodologyPsychologySocial psychologyPolitical sciencePoliticsComputer scienceLinguistics

Abstract

fetched live from OpenAlex

This paper introduces a new critical peace methodology—Relational Critical Discourse Analysis. For research to contribute to the well-being of people and their societies, traditional research methodologies need to be examined for biases and contributions to societal harm, and new approaches that contribute to just and equitable cultures need to be developed. As two researchers from dominant, privileged populations, we challenged ourselves to do this by creating and employing Relational Critical Discourse Analysis, a new research methodology that provides space for diverse perspectives and emphasizes the researchers’ interconnectedness with their participants. In this paper we describe the methodology and examine how, within one case study, it increased our ability to (a) listen deeply to participants and (b) be personally impacted by what participants are saying.

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.

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)0.022
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
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 score0.160
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.896
GPT teacher head0.760
Teacher spread0.136 · 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