Bibliographic record
Abstract
<JATS1:p>Making Taste Public takes an ethnographic approach to show how social relations shape – and are shaped by – the taste of food. Recognizing that different cultures have different taste preferences and flavour principles embedded in cuisine, editors Carole Counihan and Susanne Højlund ask how these differences are generated. The editors have compiled 14 chapters to show how specific influences become a part of our sensorial apparatus and identity through shared experiences of making, eating, and talking about food.</JATS1:p> <JATS1:p>Using case studies from Asia, Europe and America, the book presents a theory of how taste is made public through everyday practices. The authors are exploring how place, production methods and cooking techniques create tastes. They discuss the criteria determining good and bad tastes, and how tastes and memories evolve over time. Subjects such as how values can be embedded in taste, and the role of taste education in food movements, homes, and schools are explored. The different chapters examine definitions and mobilizations of taste in different institutions, public places, and regions around the world to reveal ethnographic understandings of how people learn, experience, and share taste.</JATS1:p> <JATS1:p>With contributions spanning the Solomon Islands, Denmark, Japan, Canada, France, the USA, and Italy, Making Taste Public is a fascinating account of how our sense of taste is continuously shaped and re-shaped in relation to social and cultural context, societal and environmental premises. The book will interest anyone studying anthropology, sociology, food studies, sensory studies and human geography.</JATS1:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.091 | 0.031 |
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".