Psychology gets in the game: sport, mind, and behavior, 1880-1960
Bibliographic record
Abstract
Chapter 1. Introduction Christopher D. Green (York University, Toronto), and Ludy T. Benjamin (Texas A and M University) Chapter 2. The Dawn of Sport in Europe 1880 - 1930. Gunther Baumler (Technische Hochschule Munchen, Germany) Chapter 3. E. W. Scripture and the Application of New Psychology Methodology to Athletics. C. James Goodwin (Western Carolina University) Chapter 4. Norman Triplett and The Dawning Of Sport Psychology. Stephen F. Davis (Emporia State University, Kansas), Matthew T. Huss (Creighton University, Nebraska), and Angela H. Becker (Indiana University-Kokomo) Chapter 5. Early Research on the Acquisition of Skill in Archery by Karl S. Lashley and John B. Watson. Donald A. Dewsbury (University of Florida) Chapter 6. and Baseball: The Testing of Babe Ruth. Alfred H. Fuchs (Bowdoin College, Maine) Chapter 7. An Offensive Advantage: The Football Charging Studies at Stanford University. Frank G. Baugh (William Carey University, Mississippi) and Ludy T. Benjamin, Jr. (Texas A and M University) Chapter 8. Coleman Roberts Griffith: Father of North American Sport Psychology. Christopher D. Green (York University, Toronto) Chapter 9. Paul Brown: Bringing Psychological Testing to Football. Stephen T. Graef (University of Akron), Alan S. Kornspan (University of Akron), and David Baker (University of Akron) Chapter 10. Enhancing Performance in Sport: The Use of Hypnosis and Other Psychological Techniques in the 1950s and 1960s. Alan S. Kornspan (University of Akron) Chapter 11. Conclusion: The Proper History of Sport Psychology. Christopher D. Green (York University, Toronto) and Ludy T. Benjamin, Jr. (Texas A and M 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".