Bibliographic record
Abstract
Citation (2015), "List of Contributors", Measurement of Poverty, Deprivation, and Economic Mobility (Research on Economic Inequality, Vol. 23), Emerald Group Publishing Limited, Bingley, pp. vii-ix. https://doi.org/10.1108/S1049-258520150000023015 Publisher: Emerald Group Publishing Limited Copyright © 2015 Emerald Group Publishing Limited Sabina Alkire Oxford Poverty and Human Development Initiative (OPHI), Department of International Development, University of Oxford, Oxford, UK; Economics Department, Elliott School of International Affairs, George Washington University, Washington, DC, USA Eirini Andriopoulou Hellenic Ministry of Finance, Council of Economic Advisors, Unit of Economic Research and Analysis, Athens, Greece Luis Beccaria Institute of Sciences, National University of General Sarmiento, Buenos Aires, Argentina; Faculty of Economics, University of Buenos Aires, Buenos Aires, Argentina Olga Cantó Department of Economics, Universidad de Alcalá, Madrid, Spain Andrew E. Clark Paris School of Economics (PSE), Paris, France; Centre National de la Recherche Scientifique (CNRS), Paris, France Conchita D’Ambrosio INSIDE, Université du Luxembourg, Esch/Belval, Luxembourg Manuel Espro Institute of Sciences, National University of General Sarmiento, Buenos Aires, Argentina Taryn Ann Galloway Research Department, Statistics Norway, Oslo, Norway Thesia I. Garner Bureau of Labor Statistics, Washington, DC, USA Simone Ghislandi Department of Policy Analysis and Public Management, Bocconi University, Milan, Italy Carlos Gradín Department of Applied Economics, University of Vigo, Vigo, Spain; EQUALITAS, Vigo, Spain Björn Gustafsson Department of Social Work, University of Gothenburg, Gothenburg, Sweden Andrew Heisz Income Statistics Division, Statistics Canada, Ottawa, Canada Markus Jäntti Swedish Institute for Social Research (SOFI), Stockholm University, Stockholm, Sweden Stephan Klasen Department of Economics, University of Göttingen, Göttingen, Germany Roxana Maurizio Institute of Sciences, National University of General Sarmiento, Buenos Aires, Argentina; National Council for Science and Technology, Buenos Aires, Argentina Geranda Notten Graduate School of Public and International Affairs, University of Ottawa, Ottawa, Canada Torun Österberg Department of Social Work, University of Gothenburg, Gothenburg, Sweden Peder J. Pedersen Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark David O. Ruiz Department of Economics, Universidad del Valle, Cali, Colombia; Department of Economics, Universidad de Alcalá, Madrid, Spain Kathleen S. Short US Census Bureau, Washington, DC, USA Eva M. Sierminska Graduate Studies Program, Luxembourg Institute of Socio-Economic Research (LISER, formerly CEPS/INSTEAD), Esch-sur-Alzette, Luxembourg Jerry Situ Income Statistics Division, Statistics Canada, Ottawa, Canada Van Q. Tran Faculty of Economic Science, University of Göttingen, Göttingen, Germany; Faculty of Economics, University of Economics and Law, Vietnam National University, Ho Chi Minh City, Vietnam Panos Tsakloglou Department of International and European Economic Studies, Athens University of Economics and Business, Athens, Greece Philippe Van Kerm Living Conditions Department, Luxembourg Institute of Socio-Economic Research (LISER, formerly CEPS/INSTEAD), Esch-sur-Alzette, Luxembourg Gustavo Vázquez Institute of Sciences, National University of General Sarmiento, Buenos Aires, Argentina Book Chapters Measurement of Poverty, Deprivation, and Economic Mobility Research on Economic Inequality Measurement of Poverty, Deprivation, and Economic Mobility Copyright Page List of Contributors About the Editors About the Authors Introduction Poverty Profiles and Well-Being: Panel Evidence from Germany Once Poor, Always Poor? Do Initial Conditions Matter? Evidence from the ECHP Factors Associated with Poverty and Indigence Mobility in Five Latin American Countries The Contribution of Income Mobility to Economic Insecurity in the US and Spain during the Great Recession The Role of Skills in Understanding Low Income in Canada Immigrant Child Poverty – The Achilles Heel of the Scandinavian Welfare State Rural Poverty and Ethnicity in China Static and Dynamic Disparities between Monetary and Multidimensional Poverty Measurement: Evidence from Vietnam Hardship, Debt, and Income-Based Poverty Measures in the USA Modeling the Joint Distribution of Income and Wealth
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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 | 0.027 |
| 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 | 0.011 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.743 | 0.751 |
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".