Bibliographic record
Abstract
Citation (2014), "List of Contributors", Motivational Interventions (Advances in Motivation and Achievement, Vol. 18), Emerald Group Publishing Limited, Bingley, pp. vii-viii. https://doi.org/10.1108/S0749-742320140000018013 Publisher: Emerald Group Publishing Limited Copyright © 2014 Emerald Group Publishing Limited Lily Austin Swarthmore College, Swarthmore, PA, USA Jessica E. Bachrach Swarthmore College, Swarthmore, PA, USA Matthew L. Bernacki University of Nevada, Las Vegas, NV, USA Elizabeth Canning University of Wisconsin–Madison, Madison, WI, USA Alpha Chau Swarthmore College, Swarthmore, PA, USA Sung Hyeon Cheon Kangwon National University, Gangwon-do, South Korea Judith G. Chipperfield University of Manitoba, Winnipeg, Canada Geoffrey Cohen Stanford University, Stanford, CA, USA Melissa S. Emmerson Swarthmore College, Swarthmore, PA, USA Hanoch Flum Ben Gurion University of the Negev, Beer Sheva, Israel John T. Guthrie University of Maryland, College Park, MD, USA Jeremy M. Hamm University of Manitoba, Winnipeg, Canada Judith M. Harackiewicz University of Wisconsin–Madison, Madison, WI, USA Steve Hladkyj University of Manitoba, Winnipeg, Canada Amy N. Ho University of Maryland, College Park, MD, USA Janet S. Hyde University of Wisconsin–Madison, Madison, WI, USA Avi Kaplan Temple University, Philadelphia, PA, USA Brian R. King Swarthmore College, Swarthmore, PA, USA Amanda Mason-Singh University of Maryland, College Park, MD, USA Daphna Oyserman University of Southern California, Los Angeles, CA, USA Reinhard Pekrun University of Munich, Munich, Germany Raymond P. Perry University of Manitoba, Winnipeg, Canada Johnmarshall Reeve Korea University, Seoul, South Korea K. Ann Renninger Swarthmore College, Swarthmore, PA, USA Kathryn R. Riley Swarthmore College, Swarthmore, PA, USA Robert W. Roeser Portland State University, Portland, OR, USA Arielle Silverman University of Colorado – Boulder, Boulder, CO, USA Mirit Sinai Ben Gurion University of the Negev, Beer Sheva, Israel Samantha J. Stevens Swarthmore College, Swarthmore, PA, USA Yoi Tibbetts University of Wisconsin–Madison, Madison, WI, USA Julianne C. Turner University of Notre Dame, Notre Dame, IN, USA Candace Walkington Southern Methodist University, Dallas, TX, USA Allan Wigfield University of Maryland, College Park, MD, USA Book Chapters Motivational Interventions Advances in Motivation and Achievement Advances in Motivation and Achievement Copyright Page List of Contributors Preface Attribution-Based Treatment Interventions in Some Achievement Settings Intervening to Improve Children’s Reading Motivation and Comprehension: Concept-Oriented Reading Instruction Harnessing Values to Promote Motivation in Education Going Beyond the “Whoa! That’s Cool!” of Inquiry: Achieving Science Interest and Learning with the ICAN Intervention Motivating Students by “Personalizing” Learning around Individual Interests: A Consideration of Theory, Design, and Implementation Issues Fostering Positive Narratives: Social-Psychological Interventions to Maximize Motivation in the Classroom and Beyond Identity-Based Motivation: Core Processes and Intervention Examples Design-Based Interventions for Promoting Students’ Identity Exploration within the School Curriculum An Intervention-Based Program of Research on Teachers’ Motivating Styles Theory-Based Interventions with Middle-School Teachers to Support Student Motivation and Engagement The Emergence of Mindfulness-Based Interventions in Educational Settings
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.741 | 0.730 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".