Neuroscience and the construction of a new child in early childhood education in Indonesia: A neoliberal legacy
Bibliographic record
Abstract
Neuroscience has become a new ‘truth’ in early childhood education across the globe, including in Indonesia. This article aims to demonstrate how the alignment of neuroscience discourse and the legacy of neoliberalism constructs a new form of childhood in Indonesia. The conceptual framework of brain science, predicated on biological determinism, suggests that the brain will significantly influence not only children’s development in the present but also will have an impact in the future. Neuroscience is also based on the idea of transparency. Beneath this conceptual framework lies the idea that a child’s mind can be made visible through both technological means and standardized development measures. Global neoliberal discourse reinforces this techno-scientific approach through the concept that stimulating children’s development facilitates economic growth in a country. This instrumental use of child development contrasts with the paradigm which emphasizes children’s agency. This article is based on ongoing and previous fieldwork from both authors. Using Foucault’s concept of discourse and disciplinary power, the authors argue that neuroscience has become the truth that hides societal issues such as poverty as well as becomes a form of surveillance that constructs a child as being open to the adult gaze and surveillance. The findings will also illuminate the tension and negotiation between local values and global values in assembling a new form of childhood in Indonesia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".