Bibliographic record
Abstract
In this paper we suggest that discussions about the identity of recipes should be based on a distinction between four categories of recipes.The central feature that we use to single out a category is the type of relationship that a recipe bears to its author.The first category comprises "open recipes" like wine, pizza, or salad, which come in taxonomic layers and are structurally open for new authors to reshape them.The second category comprises "institutional recipes," namely those whose authors typically form consortium-like institutions, such as Champagne wines or Quebec maple syrup.The third category comprises "brand recipes" like Coca-Cola, Nutella, or Big Mac, whose names connote rather than denote recipes.Finally, the fourth category comprises "flagship recipes," which include all the personal renditions of a recipe whose identity is strongly bound to individual authors.Besides its theoretical value, the classification we put forward is offered as a ground for settling legal disputes about recipes, evaluating charges of cultural appropriation that concern recipes, and guiding consumers, producers, and policy makers when they think about foods and diets.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".