Introduction: A New Approach to the Study of Emotional Development
Bibliographic record
Abstract
Emotional development traces a detailed pattern across the lifespan. This pattern is comprised of periods of rapid change alternating with periods of consolidation and stability, self-amplifying individual variations on universal developmental themes, and progressive complexity in feelings, thoughts, personality, behavior, and self-regulation. We know that the patterning of emotional development is not a direct expression of some species-specific or genetic program. Nor is it simply a readout of socialization practices, family experience, or any other set of environmental contingencies. Despite our attempts to predict it, emotional development is indeterminate and malleable at almost any age. Despite our normative classifications, it is characterized by idiosyncratic and unique trajectories. In short, emotional development is organized and orderly without being prespecified or programmed. How do we account for its intrinsic organization? How does emotional development achieve its coherence, complexity, and patterning without design or instruction? As in other developmental domains, the best answer to this question seems to depend on principles that extend well beyond developmental psychology. These principles express a new approach to change and novelty across the natural sciences, an approach variously called nonlinear dynamical systems theory (or dynamic systems theory, for short), complex systems theory, or chaos theory. At the heart of this perspective is the idea that natural systems behave very differently than the simple, idealized systems so well described by Newtonian mechanics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".