Bibliographic record
Abstract
Preface ( Richard M. Lerner, Tufts University). Contributors. 1 Life Span: Concepts and Issues (Willis F. Overton, Temple University). 2 Emphasizing Intraindividual Variability in the Study of Development Over the Life Span (John R. Nesselroade, The University of Virginia Peter C. M. Molenaar, The Pennsylvania State University). 3 What Life-Span Data Do We Really Need? (John J. McArdle, University of Southern California). 4 Brain Development: An Overview (Philip David Zelazo and Wendy S. C. Lee, University Minnesota). 5 Biology, Evolution and Psychological Development (Gary Greenberg, Wichita State University Ty Partridge, Wayne State University). 6 The Dynamic Development of Thinking, Feeling and Acting Over the Life Span (Michael F. Mascalo, Merrimack College Kurt W. Fischer, Harvard University). 7 Structure and Process in Life-Span Cognitive Development (Ellen Bialystok, York University, Fergus I. M. Craik, Rotman Research Institute). 8 Fluid Cognitive Abilities and General Intelligence: A Life-Span Neuroscience Perspective (Clancy Blair, New York University). 9 Memory Development Across the Life Span (Peter A. Ornstein, University of North Carolina Leah L. Light, Pitzer College). 10 The Development of Mental Processing (Andreas Demetriou, University of Cyprus Antigoni Mouyi, Pedagogical Institute of Cyprus George Spanoudis, University of Cyprus). 11 The Development of Representation and Concepts (Ulrich Muller, University of Victoria Timothy P. Racine, Simon Fraser University). 12 Development of Deductive Reasoning Across the Life Span (Robert B. Ricco, California State University at San Bernardino). 13 Development of Executive Function Across the Life Span (Sophie Jacques, Dalhousie University Stuart Marcovitch, University of North Carolina at Greensboro). 14 Language Development (Brian MacWhinney, Carnegie Mellon University). 15 Self-Regulation: The Integration of Cognition and Emotion (Megan M. McClelland, Oregon State University Claire Cameron Ponitz, University of Virginia Emily E. Messersmith, University of North Carolina at Chapel Hill Shauna Tominey, Oregon State University). 16 The Development of Morality: Reasoning, Emotions, and Resistance (Elliot Turiel, University of California, Berkeley). 17 The Development of Social Understanding: A Relational Perspective (Jeremy I. M. Carpendale, Simon Fraser University Charlie Lewis, Lancaster University). 18 The Emergence of Consciousness and Its Role in Human Development (Michael Lewis, UMDNJ Robert Wood Johnson Medical School). 19 The Development of Knowing (Michael J. Chandler and Susan A. J. Birch, The University of British Columbia). 20 Spatial Development (Marina Vasilyeva, Boston College Stella F. Lourenco, Emory University). 21 Gesturing Across the Life Span (Susan Goldin-Meadow, University of Chicago Jana M. Iverson, University of Pittsburgh). 22 Developmental Psychopathology Self, Embodiment, Meaning: A Holistic-Systems Perspective (Sebastiano Santostefano, Private Practice, Boston, MA). 23 The Meaning of Wisdom and Its Development Throughout Life (Tzur M. Karelitz, Education Development Center, Inc., Newton, MA Linda Jarvin and Robert J. Sternberg, Tufts University). 24 Thriving Across the Life Span (Matthew J. Bundick, William Damon and David S. Yeager, Stanford University Pamela Ebstyne King, Fuller Theological Seminary). Author Index. Subject Index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.271 | 0.142 |
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".