Food, Tourism and Health in Italy: A Case Study of Institutional Food Glossaries in the 1960s
Bibliographic record
Abstract
This article explores the intersection of health, well-being, food, language, and institutional tourism discourse through a comparative analysis of three mid-twentieth-century English-language food glossaries produced by ENIT (the Italian Tourist Board): At Table in Italy 1, At Table in Italy 2, and Eating in Italy. These glossaries are examined as linguistic artefacts that encode both terminological and cultural knowledge, providing insight into how Emilia-Romagna’s culinary identity was constructed, mediated, and promoted for an Anglophone tourist audience. Drawing on scholarship in terminology studies, food discourse, and tourism linguistics, the study frames these glossaries as hybrid communicative tools that combine lexical precision with narrative, ethnographic, and promotional functions. It argues that such texts exceed the referential aims of traditional glossaries, instead functioning as discursive instruments of cultural diplomacy and gastronomic branding. Special attention is given to how terms are defined, glossed, and contextualized within wider discourses of health and well-being. The analysis shows how these glossaries articulate a symbolic convergence between nourishment, heritage, and leisure, contributing to a broader understanding of food as both a linguistic and cultural practice in tourism-mediated contexts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".