Review: <i>Sugarcane and Rum: The Bittersweet History of Labor and Life on the Yucatán Peninsula</i>, by John R. Gust and Jennifer P. Mathews
Bibliographic record
Abstract
Book Review| May 01 2021 Review: Sugarcane and Rum: The Bittersweet History of Labor and Life on the Yucatán Peninsula, by John R. Gust and Jennifer P. Mathews Sugarcane and Rum: The Bittersweet History of Labor and Life on the Yucatán Peninsula, John R. Gust and Jennifer P. Mathews, Tucson: University of Arizona Press, 2020 192 pp. $29.95 (paper); $29.95 (eBook) Valeria Mantilla Morales Valeria Mantilla Morales University of Toronto Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2021) 21 (2): 116–117. https://doi.org/10.1525/gfc.2021.21.2.116 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 Valeria Mantilla Morales; Review: Sugarcane and Rum: The Bittersweet History of Labor and Life on the Yucatán Peninsula, by John R. Gust and Jennifer P. Mathews. Gastronomica 1 May 2021; 21 (2): 116–117. doi: https://doi.org/10.1525/gfc.2021.21.2.116 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 For many, the Yucatán Peninsula is synonymous with Cancún, a haven from daily life for millions of tourists who fly to this Mexican city each year for cocktails and sun. However, John Gust and Jennifer Mathews want to take us on a different journey to unveil the labor histories that have made up life on the Yucatán from the sixteenth century, arguing that sugar and rum are crucial to this journey. Sugarcane and Rum reminds us that the peninsula has been home to many Maya natives, as well as Chinese, Italians, and other migrants, whose labor has been exploited for the cultivation of sugarcane and henequen (the fibers from an agave plant) to make the very coveted rum. In their survey of the labor systems associated with sugarcane cultivation and rum production from the sixteenth century to the present... © 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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.025 |
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".