Culinary Recipes as a Textual Genre: An Analysis of Their Structure and Procedural Instructions
Bibliographic record
Abstract
Culinary recipes have only recently been studied as a distinct textual genre (Cornbleet & Carter, 2001; Garzone, 2017). Recognisable by their structured format (Baker, 2006), they have bipartite structures and occupy a unique linguistic space that cannot be fully integrated into General English text types (Norrick, 1983). Their language is highly typified and reflects the effects of digital transformation. The text of a recipe may be selected from cookery books or online, and its choice is based on the author’s perceived reliability adopting an accurate linguistic code, literary genre conventions, and specialised indices. The textual interpretation of a recipe involves a mental reconstruction of the process of preparing edible products, from the inference of its presupposed truthfulness to the sense of readiness required to carry it out practically. Food recipes are therefore analysed in this paper as organised models of texts (Enkvist, 1981). The study examines a mini-corpus of recipes from Jamie Oliver’s cookbook, Jamie’s Great Britain: Over 130 Reasons to Love Our Food (2011), and his popular website, considering two research questions: 1) How do recipes function as structured models in their rhetorical patterns to facilitate the reader’s understanding and execution of the instructions? 2) How does the verb “put” convey procedural instructions?
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".