Reflections on adaptive behavior : essays in honor of J.E.R. Staddon
Bibliographic record
Abstract
John Staddon has devoted his long and distinguished career to the study of the adaptive function and mechanisms of learning. He did his graduate work at the famous Skinner Lab at Harvard in the early 1960s (supervised by Richard Herrnstein, who did his doctoral work with B. F. Skinner), but his work can be characterized as behaviorism. Staddon, now at Duke University, believes that experimental analysis is never enough to make sense of behavior and that theoretical is also required. Staddon's imagination has distinguished his work over the years and has influenced the field. Staddon is not afraid to deviate from the norm: when psychologists were maintaining their distance from behavioral psychology, Staddon was promoting optimality theories. Optimality theories in psychology are now commonplace. In this volume, Staddon's colleagues and former students discuss topics that have been important in his work: behavioral ability and choice, memory, time and models (the subject of his work at Harvard), and behaviorism. They also reflect on Staddon's influence on their own work and the evolution of their thinking on these topics. ContributorsGiulio Bolacchi, Daniel T. Cerutti, Mircea Ioan Chelaru, J. Mark Cleaveland, Robert H. I. Dale, Rebecca A. Dixon, Valentin Dragoi, Stephen Gray, Jennifer J. Higa, John M. Horner, Nancy K. Innis, Mandar S. Jog, Richard Keen, John E. Kello, Eric Macaux, Armando Machado, John C. Malone, Jr., Kazuchika Manabe, Susan R. Perry, Alliston K. ReidNancy K. Innis was Professor of Psychology at the University of Western Ontario. J. E. R. Staddon supervised her Ph.D. work at Duke University.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".