Food Atlas: Discover All the Delicious Foods of the World by G. Malerba
Bibliographic record
Abstract
Malerba, Giulia. Food Atlas: Discover All the Delicious Foods of the World, illustrated by Febe Sillani, translated by Sharon Morin. Firefly Books, 2017.This large and beautiful folio volume provides an agricultural and culinary tour of the world in the form of nearly fifty maps that cover six continents, Oceania, and fifty individual countries. The book ends with a two-page map of the world to illustrate the “food journeys” by which many familiar, staple foods were transplanted around the globe. Luca Mingolia’s maps, overlain with Febe Sillani’s hundreds of colourful illustrations, depict both the characteristic foods and ethnic dishes of each country and region. The coverage is extraordinarily comprehensive, ranging from Sweden’s repugnant-smelling Surströmming to the equally pungent Durians of southeast Asia, and from Egyptian Ful Medames to India’s Gulab Jamun.Although cast in the format of a book for older children, this fascinating volume is one from which older readers, including adults, may take pleasure and expand their culinary horizons.Highly Recommended: 4 out of 4 starsReviewer: Merrill DistadHistorian and author Merrill Distad enjoyed a four-decade career building libraries and library collections.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.189 | 0.186 |
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".