Bibliographic record
Abstract
Abstract: Following debate surrounding nominations of food practices for the United Nations Educational, Scientific and Cultural Organization’s lists of intangible cultural heritage (ICH), 10 years after the entry into force of the Convention for the Safeguarding of Intangible Cultural Heritage, we observe that the ICH lists count a growing number of food-related heritage elements. Yet food, or even gastronomy, as a cultural domain within ICH has yet to be officially recognized. However, given the trade policies arising from the new globalization, which subject peoples and the planet to imported, globalized, and standardized models and which generate an impoverishment of agricultures and food cultures, major geo-economic issues will play out around this recognition. Thus, along with identification labels of quality and origin that protect certain products and know-how from counterfeiting, other forms of protection could be put into place for the benefit of intangible food heritage inscribed on national lists and of the products, goods, services, industries, and cultural spaces in which they are embedded. From the perspective of safeguarding cultural diversity, which any inscription of ICH should lead to, these protections could operate not only through the cultural policies within safeguarding plans but also through the creation of a new binding legal instrument—“the heritage brand”—which could become an important facet of international trade law.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".