Bibliographic record
Abstract
Craig Gundersen is the Snee Family Endowed Chair at the Baylor Collaborative on Hunger and Poverty and a professor in the Department of Economics at Baylor University. Prior to joining Baylor, he was ACES Distinguished Professor in the Department of Agricultural and Consumer Economics at University of Illinois and, before that, he had positions at Iowa State University and USDA, Economic Research Service. Gundersen is currently also on the technical advisory group for Feeding America, the lead researcher on Feeding America's Map the Meal Gap project, a Round Table Fellow of the Farm Foundation, and a faculty affiliate of the Wilson Sheehan Lab for Economic Opportunities at the University of Notre Dame. From 2018 to 2022 he was the managing editor for Applied Economic Perspectives and Policy. For over 25 years, Gundersen's research has concentrated on the causes and consequences of food insecurity and on the evaluation of food assistance programs, with an emphasis on the Supplemental Nutrition Assistance Program (SNAP, formerly known as the Food Stamp Program). In terms of the causes of food insecurity, his work has included, for example, examinations of the multigenerational households, American Indians, and food bank clients. Of particular note is his invention of Map the Meal Gap, which has become the standard tool to portray the geography of food insecurity in the U.S. He also has the two most cited review papers in this area with one of them recognized as the Outstanding Paper in the 2011 volume of AEPP. In terms of the consequences of food insecurity, his work has concentrated on dispelling myths of the connection between food insecurity and obesity, on the myriad negative health outcomes of food insecurity among seniors, and on the impacts of food insecurity on mortality and health care costs. He also has the most cited review paper on the consequences of food insecurity. With respect to his work evaluating food assistance programs, his work was the first to show that, after controlling for nonrandom selection into SNAP, recipients are less likely to be food insecure than eligible nonrecipients. This result was verified in further papers he co-authored including ones that also addressed misreporting of SNAP receipt. Gundersen's research has been published in top journals across a number of fields including in ag economics (e.g., AEPP, Food Policy, AJAE), economics (e.g., Journal of Human Resources, Journal of Econometrics, Journal of Health Economics), statistics (e.g., Journal of the American Statistical Association), nutrition (e.g., Journal of Nutrition), and medicine (e.g., New England Journal of Medicine, Canadian Medical Association Journal). His research has been funded by over $20 million in external funding from over 25 grants. These funds have come from several sources including the National Institute for Food and Agriculture (NIFA), Economic Research Service (ERS), Food and Nutrition Service (FNS), Foundation for Food and Agriculture Research (FFAR), and Canadian Institutes for Health Research (CIHR). The stature of his research and the importance of the topics he pursues has led Gundersen to frequently in the media and in presentations to policymakers and program administrators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.355 | 0.163 |
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".