Productive Uncertainty and the Genomics of 'Neurodevelopmental Disorders': A Critical Discourse Analysis
Bibliographic record
Abstract
Genomics, in association with precision medicine, promises future certainty meant to ‘pinpoint’ individual differences and allow for individualized therapies. This approach to medicine is also taking place within a context in which neoliberal rationalities dominate thinking about ‘health’ and ‘normality’. My dissertation challenges the notion that the goal of biomedicine, and genomics in particular, is to attain certainty and precision. Rather, uncertainty in this context is a form of power-knowledge that is productive, circulates, and is used as a resource. This study is concerned with the use of uncertainty and uncertainty management discourses to shape genomic testing and diagnosis of neurodevelopmental disorders (NDDs). The dissertation aims to extend understandings of how the changing uncertainty discourses of genetics experts can, instead of confounding and diminishing technological and diagnostic pursuits, propel these pursuits using discursive strategies to frame what and who is ethical. Few studies have critically examined the discourses of genetics experts regarding uncertainty in this particular context. I use Foucauldian-influenced critical discourse analysis and a critical bioethics approach to carry out and examine interviews with genetics experts on uncertainty and the genomics of NDDs. My findings highlight three discourses in a field that is changing over time and is technologically dependent. First, the ‘traditional certainty’ discourse was constructed as ‘old thinking’ and as receding into the past. This is contrasted with the ‘uncertainty management’ discourse that is growing in dominance and represents a ‘modern’ approach to uncertainty production in biomedical genomics. A third discourse ‘slips’ between the two discursive poles of ‘traditional certainty’ and ‘modern uncertainty management’. This ‘terrain of slippage’ contains contradictory discourses that co-exist, highlighting tensions around shifting authority and changing technology. Through my analysis, I argue that uncertainty discourses are used to construct varying ethical subjectivities that ultimately extend the reach of genomics while diminishing the need for direct human participation in genomic testing and diagnostic decision-making. This construction culminates in the creation of the ‘innocent’ subject who strategically uses uncertainty to avoid and ignore contentious discourses in the biomedical genomics context and ‘forgets’ the disabled patient who is inconvenient to modern uncertainty management discourses.
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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 | 0.056 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.021 | 0.077 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".