Critical discursive psychology and relational ethics: <scp>A</scp> n uneasy tension?
Bibliographic record
Abstract
Abstract The debate over the ethicality of discourse analysis, particularly against the backdrop of relational ethics and the use of interview‐generated data, has recently intensified (see Hammersley, , ; Smith, ; Taylor, ). By way of extending this debate beyond the issue of what constitutes informed consent in discourse analytic research, I focus on the question of harm that might come to participants who provide data that are analysed through the lens of critical discursive psychology and who are subsequently privy to our analyses. Through the use of an actual example from my participation in a project on different ways of analysing qualitative data, I consider the ramifications of critical discursive analytic processes and goals for the ethical obligation to minimize harm to participants that might result from our analyses and for the practice of sharing qualitative analyses with participants. I conclude that choosing to reside in an uneasy tension that can exist between critical discursive psychology and relational ethics, particularly at the stages of writing, analysis, and dissemination of our work, raises questions that require further conversation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".