The science of humanity and the humanity of science: Perspectives on Ed Zigler's contributions to developmental psychopathology and the study of all children
Bibliographic record
Abstract
We present this article as a testament to Ed Zigler's commitment to science in the service of humanity and to policy based on conceptually compelling theory and methodologically rigorous science. In doing so, we highlight ways that Ed's universal and inclusive developmental world view, early training as a behaviorist, exacting scientific standards, concern for others, and appreciation of his own roots and upbringing all transformed the way that many different groups of people of all ages and backgrounds are studied, viewed, and intervened with by researchers, policy makers, and society at large. Ed's narrative of development rather than defect, universality rather than difference, and holistic rather than reductionist continues to compel us in the quest for a kinder, more inclusive, and enabling society. Conversely, Ed's behaviorist training as a graduate student also influenced him throughout his career and was essential to his career-long commitment to systemic action in the service of improving the lives of others. We cite the lessons that we, as his descendants, learned from Ed and apply them to our own areas of research with populations that Ed did not study, but had considerable interest in - persons with autism spectrum disorder and Indigenous youth.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".