Producing children in the 21st century: A critical discourse analysis of the science and techniques of monitoring early child development
Bibliographic record
Abstract
The purpose of this article is to identify the implications of commonly held ideologies within theories of child development. Despite critiques to doing so, developmental theory assumes that children's bodies are unitary, natural and material. The recent explosion of neuroscience illustrates the significance of historical, social and cultural contexts to portrayals of brain development, offering the opportunity for a critical departure in thinking. Instead, this neuroscience research has been taken up in ways that align with biomedical traditions and neoliberal values. This article uses a critical discursive approach, supported by Haraway's ideas of technoscience, to analyse a population-based early child development research initiative. This initiative organises a large-scale surveillance of children's development, operating from the premise that risks to development are best captured early to optimise children's potential. The analysis in this article shows an intermingling of health and economic discourses and clarifies how the child is a figure of significant contemporary social and political interests. In a poignant example of technobiopolitics, the collusion between health research, technologies and the state enrols health professionals to participate in the production of children as subjects of social value, figured as human capital, investments in the future, or alternatively, as waste. The analysis shows how practices that participate in what has become a developmental enterprise also participate in the marginalisation of the very children they intend to serve. Hence, there is the need to rethink practices critically and move towards innovative conceptualisations of child development that hold possibilities to resist these figurations.
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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 | 0.059 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.024 | 0.119 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| 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".