Bibliographic record
Abstract
The history of the scientific study of human development shows that conventional portrayals of development tend to be maintained until newer, better models emerge to take their place. This paradigmatic shift is much clearer in hindsight than at the time it occurs, during which the potential benefits of new formulations and their heuristic potential are explored, scrutinized, and debated. When this occurs, traditional ideas may be maintained (albeit in altered form) or integrated with new formulations, or more comprehensive changes may occur in how developmentalists fundamentally view familiar phenomena as a result of alternative models. Are we in the midst of such a shift in thinking because of the emergence of dynamic systems views of development? At present, it is difficult to say. While many of the heuristic possibilities of dynamic systems approaches are becoming apparent, their broader utility for a comprehensive developmental formulation is yet unclear, and their empirical testability is even more obscure. One way of assessing the potential value of a dynamic systems approach as a framework for developmental thinking is to apply it to a well-developed body of research in order to explore whether it offers valuable new insights, enables researchers to ask new questions, and explains perplexing findings in a manner that suggests its broader value for developmental theory. This chapter is concerned with the relevance of dynamic systems formulations to attachment theory, a field of research that has dominated the study of early sociopersonality development for more than a quarter of a century (Thompson, 1998).
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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 | 0.001 | 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 | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".