The Race against Rot: Gastronomica's New Editorial Team Weighs in on Saving Food
Bibliographic record
Abstract
Research Article| August 01 2019 The Race against Rot: Gastronomica's New Editorial Team Weighs in on Saving Food Anelyse M. Weiler, Anelyse M. Weiler Anelyse M. Weiler is College Professor of Sociology at Okanagan College and a sociology PhD candidate at the University of Toronto. Her research focuses on labor, immigration, and sustainability across the food system. She has published in journals such as International Migration, Antipode, and Journal of Agriculture, Food Systems, and Community Development. Currently, she is conducting a case study on the emerging craft cider industry in the Pacific Northwest. Search for other works by this author on: This Site PubMed Google Scholar Sarah Elton, Sarah Elton Sarah Elton is Assistant Professor of Sociology at Ryerson University in Toronto. She researches food systems, climate change, and food sovereignty. She is the author of four books about food including Consumed: Food for a Finite Planet and, for kids, Meatless? A Fresh Look at What You Eat. Search for other works by this author on: This Site PubMed Google Scholar Josée Johnston Josée Johnston Joseée Johnston is Professor of Sociology at the University of Toronto. She is the co-author of Foodies with Shyon Baumann, as well as Food and Femininity with Kate Cairns. She has published recent articles in venues such as Social Forum, Journal of Consumer Culture, Theory and Society, and British Journal of Sociology. Her major substantive interest is the sociological study of food, which is a lens for investigating questions relating to consumer culture, gender, sustainability, and inequality. Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2019) 19 (3): 1–5. https://doi.org/10.1525/gfc.2019.19.3.1 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Anelyse M. Weiler, Sarah Elton, Josée Johnston; The Race against Rot: Gastronomica's New Editorial Team Weighs in on Saving Food. Gastronomica 1 August 2019; 19 (3): 1–5. doi: https://doi.org/10.1525/gfc.2019.19.3.1 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. © 2019 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, http://www.ucpress.edu/journals.php?p=reprints.2019 Article PDF first page preview Close Modal You do not currently have access to this content.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.054 | 0.021 |
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".