Relational Critical Discourse Analysis: A Methodology to Challenge Researcher Assumptions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.229 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.016 | 0.047 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".