Postcritical discourse analysis: examining the case of the student well-being discourse
Bibliographic record
Abstract
Abstract This article examines how the critical tradition initiated by Nietzsche and pursued through poststructuralism might be compatible with what is currently being described as postcritique. It does so by looking at the example of critical discourse analysis (CDA). The first section gives some indications about the state of the methodology currently known as critical discourse analysis and introduces what a ‘postcritical’ reaction could look like. The second section focuses on a concrete example and presents the main critical literature about the student well-being discourse, showing that it generally limits itself to a debunking attitude. The third section explores how the affirmative and creative dimension of critique, its ‘postcritical’ dimension, has been and could be put forward to contribute to what could be called a postcritical discourse analysis methodology
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.042 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".