The Social Effects of Gestational Diabetes in "High-risk Ethnic Groups"
Bibliographic record
Abstract
This ethnographically-informed doctoral study examines the social effects of gestational diabetes (GD) in "high-risk ethnic groups" in order to elucidate how contested categories of disease, risk, and race cross-articulate in authoritative texts, clinical practice, and everyday realities of women of colour. The questions that guide this dissertation study are organized around truth discourses, strategies of intervention, and modes of subjectification: What kinds of discourses are employed to constitute knowledge about GD, risk, and race/ethnicity? What types of subjects are constructed through discourses on GD in "high-risk ethnic groups?" How are race-based risk discourses accomplished locally in the clinical setting? How do women respond to, engage with, and resist race-based risk discourses and practices pertaining to GD? And how do such discourses and practices shape women's subjectivities from diagnosis to post-partum? Study methods include discourse analysis of three authoritative texts, participant observation in two diabetes education centres in Southern Ontario, and interviews conducted with twelve women of colour in a three time sequence (after diagnosis, before delivery and post-partum). Data generated from these multiple sources were analyzed and interpreted through Foucauldian theoretical concepts of biopower, governmentality, and subjectification. The findings reveal that discourses are neither neutral nor value-free but infused with racial and moral assumptions. Race-based risk discourses are accomplished in the clinical setting through a variety of strategies that reproduce racial logics by rendering power relations invisible. However, women in this study engaged with and resisted disciplinary practices in enabling and constraining ways that contributed to the formation of an emergent type of racialized subject. I argue that discourses and disciplinary practices participate in the processes of racialization and subjectification which may paradoxically produce unintended effects of contributing to the problem of diabetes. I conclude by calling for greater reflexivity beyond racialization and medicalization.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".