Measurement and Mismeasurement of Social Development in Infants Later Diagnosed with Autism Spectrum Disorder
Bibliographic record
Abstract
Autism spectrum disorder (autism) is a common and heterogeneous neurodevelopmental disorder of genetic origins defined by challenges in social communication and clusters of restrictive and repetitive behaviors. An emerging hypothesis of autism pathogenesis describes symptoms as the results from deviations from normative developmental processes. In this account, symptoms represent the outcome of variable instantiation of genetic liabilities – in terms of dosage and timing – leading to disruptions in the developmental trajectories of foundational social adaptive skills. Given the fast pace of change in behavior and brain development in the first two years of life, we pose that the currently prevalent cross-sectional experimental designs are ill-suited to capture changes from normative benchmarks that might be small at any data point but which inexorably and cumulatively increase divergences in developmental trajectories that ultimately culminate in the unmistakable cluster of atypical behaviors we now call autism. We argue that only densely-sampled longitudinal experimental designs can capture the underlying dynamic processes moving the individual child’s development towards or away from normative benchmarks. We illustrate this phenomenon via a detailed example in which a cross-sectional comparison between a clinical and a control cohort failed to find differences, which could only be detected by ascertaining that the developmental trajectory of one cohort was moving upwards while the other was moving downwards, with the developmental lines intersecting at the cross-sectional data point. We conclude by magnifying Karmiloff-Smith’s assertion, oft-quoted but seldom followed, that “development itself is the key to understanding developmental disorders” [1].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".