Bibliographic record
Abstract
Book Review| May 01 2021 Review: Politics of Food, edited by Aaron Cezar and Dani Burrows Politics of Food, Edited by Aaron Cezar and Dani Burrows, Berlin and London: Sternberg Press and Delfina Foundation, 2019, 240 pp. Color illustrations throughout. £20.00, €24.00, $29.00 (paper) Pamela Tudge Pamela Tudge Concordia University Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2021) 21 (2): 122–124. https://doi.org/10.1525/gfc.2021.21.2.122 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Pamela Tudge; Review: Politics of Food, edited by Aaron Cezar and Dani Burrows. Gastronomica 1 May 2021; 21 (2): 122–124. doi: https://doi.org/10.1525/gfc.2021.21.2.122 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentGastronomica Search Politics of Food is the latest example representing the growing contribution from artists and artist collectives to food scholarship. In this collection, artists, activists, chefs, and scholars critically assess our food systems through art projects, conversations, and topical essays. The book stems from the Delfina Foundation’s program of the same name, which the editors Aaron Cezar and Dani Burrows have facilitated for several years. It represents a selection from the program’s art residences, public food events, and collective meals, with additional contributions inspired by its mandate. The book’s central contribution is the art featured from twenty-seven artists which provide a glimpse into numerous projects. Politics is loosely defined through the various subjects, ranging from artistic reflections on food and identity to broader topics such as food sovereignty, commensality, and haute cuisine. Collectively, the editors aim to use food to... © 2021 by The Regents of the University of California2021 You do not currently have access to this content.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".