A Survey of Dynamic Systems Methods for Developmental Psychopathology
Bibliographic record
Abstract
Abstract A survey of dynamic systems (DS) methods appropriate for testing systems‐based models in developmental psychopathology is provided. First, we review the rationale for developing new methods for the field. In line with other investigators, we highlight the fundamental incompatibility between developmentalists' organismic, open systems models and the mechanistic research methods with which these models are tested. We explain key DS principles and discuss their commensurability with developmental psychopathologists' core theoretical concerns. Next, we provide an updated survey of research designs and methodological techniques currently being used and refined by developmental DS researchers. The strengths and limitations of each approach are discussed throughout this review. Finally, we elaborate on one specific dynamic systems method,state space grids, which addresses many of the limitations of previous DS techniques and may prove useful for the discipline. This approach was developed as a middle road between DS methods that are mathematically demanding on one hand and purely descriptive on the other. We review examples of developmental and clinical studies that have applied state space grids, and we make suggestions for future analyses. Finally, we conclude with some implications for the application of this new methodology to change processes in prevention and treatment research.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".