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 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 | 0.139 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.177 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".