Extending the methodology of critical discourse analysis using Haraway's figurations: The example of <i>The Monstrous Perpetrator</i> within contemporary responses to child neglect and abuse
Bibliographic record
Abstract
Critical discursive analyses offer possibilities for equity-oriented research, and are a resource for addressing resistant social problems, such as child neglect and abuse (CN&A). A key challenge for discourse analysts in health disciplines is the tensions between materiality and social constructions, particularly at the site of the body. This paper describes how Donna Haraway's ideas of figuration and technobiopower can augment critical discourse analysis to address this tension. Technobiopower, an intensification of biopower in the context of technoscience, is seen as underpinning the melding of material and semiotic practices. The subject is no longer a material body, but a hybrid body that exists in tropic figuration between the real and unreal. This paper uses an analysis of the figuration of The Monstrous Perpetrator from a study of nursing responses to CN&A to illustrate how Haraway's figuration aligns with and provides an analytical tool to extend critical discursive analyses. Specifically, this methodology offers new ways to identify the discursive qualities of bodies, and how material aspects of bodies are exaggerated, concealing their hegemonic ideologies and discriminatory effects. By identifying discourses within or inscribed upon the body, they can be disrupted, opening new possibilities for social change.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.016 | 0.060 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".