Strategic considerations in the search for transactional processes: Methods for detecting and quantifying transactional signals in longitudinal data
Bibliographic record
Abstract
Over the last four decades the transactional model has emerged as a central fixture of modern developmental science. Despite this, we are aware of no principled approach for determining (a) whether it is actually necessary to invoke transactional mechanisms to explain observed patterns of stability in a given domain of adaptive functioning and (b) the extent to which transactional processes, once identified in aggregate, are accounted for by measured domains with which an aspect of adaptive functioning is theoretically in transaction. Leveraging the fact that transactional mechanisms produce excess stability in an outcome domain above and beyond autoregressive processes, along with the basic logic of mediational analysis, we introduce two novel indexes for studying transactional processes strategically. We apply these metrics to data from the NICHD Study of Early Child Care and Youth Development cohort on mother- and teacher-reported externalizing problems and social competence along with teacher-reported and objective assessments of academic skills acquired in Grades 1, 3, and 5. During this developmental period we find that (a) transactional contributions to stability are strongest for teacher-reported outcomes, next strongest for mother-reported outcomes, and relatively weak for objective assessments of academic skills and (b) observed maternal sensitivity (but not child-reported friendship quality) accounts for a modest proportion of the total transactional effects operative in most of the domains of adaptive functioning we studied. Discussion focuses on extending the logic of our approach to additional waves of measurement.
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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 | 0.236 | 0.572 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".