Review: <i>Women Who Dig: Farming, Feminism, and the Fight to Feed the World</i>, by Trina Moyles, photographs by KJ Dakin
Bibliographic record
Abstract
Book Review| May 01 2021 Review: Women Who Dig: Farming, Feminism, and the Fight to Feed the World, by Trina Moyles, photographs by KJ Dakin Women Who Dig: Farming, Feminism, and the Fight to Feed the World, Trina Moyles (Photographs by KJ Dakin), Regina, SK: University of Regina Press, 2018 271 pages. Illustrations. $34.95 (paper) Claire Perttula Claire Perttula Balsillie School of International Affairs Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2021) 21 (2): 118–119. https://doi.org/10.1525/gfc.2021.21.2.118 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 Claire Perttula; Review: Women Who Dig: Farming, Feminism, and the Fight to Feed the World, by Trina Moyles, photographs by KJ Dakin. Gastronomica 1 May 2021; 21 (2): 118–119. doi: https://doi.org/10.1525/gfc.2021.21.2.118 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 Women Who Dig: Farming, Feminism, and the Fight to Feed the World is Trina Moyles’s first book. As a freelance journalist, photographer, human rights activist, and community organizer, Moyles has a unique perspective and set of skills to bring to her writing. She combines these skills for a jargon-free, beautifully designed book touching on important issues facing women working the land. The testimonials in this book tell of motherhood, activism, gender-based violence, independence, self-sufficiency, rebirth, resistance, support, and protecting and nourishing land, communities, history, and culture. Moyles “wanted to engage as a storyteller: to witness, learn, participate, and listen with all of [her] senses.…[She] hungered, only, for stories and all of their nuances” (p.xxv). Despite her efforts, in the end her storytelling hurt her cause; by trying so hard to write something beautiful, the very real injustices that women... © 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 | 0.001 | 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 | 0.000 | 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".