The Food and COVID-19 NYC Archive: Mapping the Pandemic's Effect on Food in Real Time
Bibliographic record
Abstract
Research Article| November 01 2020 The Food and COVID-19 NYC Archive: Mapping the Pandemic's Effect on Food in Real Time Amy Bentley, Amy Bentley Amy Bentley is Professor of Food Studies at New York University and author of Inventing Baby Food: Taste, Health, and the Industrialization of the American Diet(University of California Press, 2014). Current research projects include a history of food in US hospitals, and the meanings and uses of food production in religious communities. Search for other works by this author on: This Site PubMed Google Scholar Stephanie Borkowsky Stephanie Borkowsky Stephanie Borkowsky is a master's student of food studies at New York University. She holds an undergraduate degree in history from McGill University. Her current research explores the relationship between food and power in colonial relations. Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2020) 20 (4): 8–11. https://doi.org/10.1525/gfc.2020.20.4.8 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Amy Bentley, Stephanie Borkowsky; The Food and COVID-19 NYC Archive: Mapping the Pandemic's Effect on Food in Real Time. Gastronomica 1 November 2020; 20 (4): 8–11. doi: https://doi.org/10.1525/gfc.2020.20.4.8 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentGastronomica Search This content is only available via PDF. © 2020 by the Regents of the University of California. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press's Reprints and Permissions web page, https://www.ucpress.edu/journals/reprints-permissions.2020 Article PDF first page preview Close Modal You do not currently have access to this content.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".