Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,355 | 0,163 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».