Image, measure, figure: a critical discourse analysis of nursing practices that develop children
Bibliographic record
Abstract
Motivated by discourses that link early child development and health, nurses engage in seemingly benign surveillance of children. These practices are based on knowledge claims and technologies of developmental science, which remain anchored in assumptions of the child body as an incomplete form with a universal developmental trajectory and inherent potentiality. This paper engages in a critical discursive analysis, drawing on Donna Haraway's conceptualizations of technoscience and figuration. Using a contemporary developmental screening tool from nursing practice, this analysis traces the effects of this tool through production, transformation, distribution, and consumption. It reveals how the techniques of imaging, abstraction, and measurement collide to fix the open, transformative child body in a figuration of the developing child. This analysis also demonstrates how technobiopower infuses nurses' understandings of children and structures developmentally appropriate expectations for children, parents, and nurses. Furthermore, it describes how practices that claim to facilitate healthy child development may inversely deprive children of agency and foster the production of normal or ideal children. An alternative ontological perspective is offered as a challenge to the individualism of developmental models and other dominant ideologies of development, as well as practices associated with these ideologies. In summary, this analysis argues that nurses must pay closer attention to how technobiopower infuses practices that monitor and promote child development. Fostering a critical understanding of the harmful implications of these practices is warranted and offers the space to conceive of human development in alternate and exciting ways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".