Bibliographic record
Abstract
Citation (2014), "List of Contributors", Factors Affecting Worker Well-being: The Impact of Change in the Labor Market (Research in Labor Economics, Vol. 40), Emerald Group Publishing Limited, Bingley, pp. vii-viii. https://doi.org/10.1108/S0147-912120140000040017 Publisher: Emerald Group Publishing Limited Copyright © 2014 Emerald Group Publishing Limited Maria Bampasidou University of Florida, USA Rodrigo Bueno University of Sao Paulo, Brazil Vincenzo Carrieri Department of Economics and Statistics and CELPE, University of Salerno, Italy; Health Econometrics and Data Group, University of York, UK Arnaud Chevalier Royal Holloway University of London, London, UK Matthias Cinyabuguma Department of Economics, University of Maryland, Baltimore County, USA Marie Connolly Département des sciences économiques, University of Quebec in Montreal, Cirpée, Cirano Cinzia Di Novi Department of Economics, Ca’ Foscari University of Venice, Italy Carlos A. Flores Department of Economics, California Polytechnic State University at San Luis Obispo, USA Alfonso Flores-Lagunes Department of Economics, State University of New York at Binghamton, USA and IZA Alan L. Gustman Department of Economics, Dartmouth College, USA Rowena Jacobs Centre for Health Economics, University of York, UK William Lord Department of Economics, University of Maryland, Baltimore County, USA Mauricio Moura George Washington University and Harvard University, USA Daniel J. Parisian Department of Economics, State University of New York at Binghamton, USA Silvana Robone Dipartimento di Scienze Economiche, Università di Bologna, Italy and Health, Econometrics and Data Group, University of York, UK Thomas L. Steinmeier Texas Tech University, USA Massimiliano Tani School of Business, UNSW Canberra, Australia and IZA Christelle Viauroux Department of Economics, University of Maryland, Baltimore County, USA Book Chapters Factors Affecting Worker Well-being: The Impact of Change in the Labor Market Research in Labor Economics Factors Affecting Worker Well-being: The Impact of Change in the Labor Market Copyright Page List of Contributors Editorial Advisory Board Preface Explaining the Revolution in U.S. Fertility, Schooling, and Women’s Work among Households Formed in 1875, 1900, and 1925 Integrating Retirement Models: Understanding Household Retirement Decisions The Role of Degree Attainment in the Differential Impact of Job Corps on Adolescents and Young Adults Insecure, Sick and Unhappy? Well-Being Consequences of Temporary Employment Contracts The Effect of Land Title on Child Labor Supply: Empirical Evidence from Brazil The Changing Time use of U.S. Welfare Recipients between 1992 and 2005 Does Higher Education Quality Matter in the UK? Business Visits and the Quest for External Knowledge
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.735 | 0.739 |
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".