The Oxford Handbook of the Economics of Food Consumption and Policy
Bibliographic record
Abstract
Historically, the challenge for humans has been to secure a sufficient supply of food to stave off hunger and starvation. As a result, much of the research on food and agriculture in the past century has focused on issues related to production efficiency, food supply, and farm profitability. In recent years, however, farmers, agribusiness, policy makers, and academics have increasingly turned their attention away from the farm and toward the food consumer and to issues related to food consumption. This handbook provides an overview of the economics of food consumption and policy and is a useful reference for academics and graduate students interested in food economics and the consumer-end of the supply chain. It is also relevant to those employed in food and agricultural industries, policy makers, and activist groups. The first section covers the application of the core theoretical and methodological approaches of the economics of food consumption and policy. The second part concentrates on policy issues related to food consumption. Several chapters focus on the theoretical and conceptual issues relevant in food markets, such as product bans, labeling, food standards, political economy, and scientific uncertainty. Additional chapters discuss policy issues of particular interest to the consumer-end of the food supply chain, such as food safety, nutrition, food security, and development. The final section serves as an introduction to particular issues and current topics in food consumption and policy. Contributors to this volume - Awudu Abdulai, University of Kiel, Germany W.L. Adamowicz, University of Alberta, Canada Luis Miguel Albisu, Agro-Food Research and Technology Centre of Aragon (CITA), Spain Frode Alfnes, Norwegian University of Life Sciences, Norway Sven M. Anders, University of Alberta, Canada Fredrik Carlsson, University of Gothenburg, Sweden Sean B. Cash, University of Wisconsin, Madison, USA Julie A. Caswell, University of Massachusetts Amherst, USA Marco Costanigro, Colorado State University, USA Sven-Olov Daunfeldt, Dalarna University, Sweden Andreas C. Drichoutis, University of Ioannina, Greece Fabrice Etile Institut National de la Recherche Agronomique, France and Paris School of Economics, France Jacinto F. Fabiosa, Iowa State University, USA John A. Fox, Kansas State University, USA Konstantinos Giannakas, University of Nebraska-Lincoln, USA Azucena Gracia, Agro-Food Research and Technology Centre of Aragon (CITA), Spain Roland Herrmann, Justus-Liebig University Giessen, Germany Sandra A. Hoffmann, Resources for the Future, Washington, DC, USA Wallace E. Huffman, Iowa State University, USA Helen H. Jensen, Iowa State University, USA Olof Johansson-Stenman, University of Gothenburg, Sweden David R. Just, Cornell University, USA Harry M. Kaiser, Cornell University, USA Christian Kuhlgatz, University of Kiel, Germany Panagiotis Lazaridis, Agricultural University of Athens, Greece Mariah L. Loureiro, Universidade de Santiago de Compostela, Spain Jayson L. Lusk, Oklahoma State University, USA Stephan Marette, UMR Economie Publique INRA-AgroParisTech, France Thomas L. Marsh, Washington State University, USA William A. Masters, Tufts University, USA Jill J. McCluskey, Washington State University, USA Pierre R. Merel , University of California-Davis, USA Rodolfo M. Nayga, Jr., University of Arkansas, USA Jonas Nordstrom, University of Copenhagen, Denmark, and Lund University, Sweden Nicholas E. Piggott, North Carolina State University, USA Kyrre Rickertsen, Norwegian University of Life Sciences, Norway Jutta Roosen, Technische Universitat Munchen, Germany Ana Isabel Sanjuan, Agro-Food Research and Technology Centre of Aragon (CITA), Spain Ted C. Schroeder, Kansas State University, USA Richard J. Sexton, University of California-Davis, USA Ian Sheldon, The Ohio State University, USA Jason F. Shogren, University of Wyoming, USA Hayden Stewart, U.S. Department of Agriculture, Economic Research Service, USA Joffre D. Swait, University of Alberta, Canada Johan F.M. Swinnen, University of Leuven, Belgium Mario F. Teisl, Professor, University of Maine, USA Ramona Teuber, Justus-Liebig University Giessen, Germany Linda Thunstrom, The Swedish Retail Institute (HUI), Sweden Glynn T. Tonsor, Kansas State University, USA Thijs Vandemoortele, University of Leuven, Belguim Joachim von Braun, Bonn University, Germany Christoph R. Weiss, Vienna University of Economics and Business, Austria Parke Wilde, Tufts University, USA Michael K. Wohlgenant,North Carolina State University, USA
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.015 |
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".