“Like, Pasta, Pizza and Stuff” – New Trends in Online Food Discourse
Bibliographic record
Abstract
This paper examines two examples of online food discourse to illustrate how the medium changes parameters established in print recipes: food blogs for written and Skype talks for oral computer-mediated discourse about food. The language of food blogs will be analysed with the help of examples from the Food Blog Corpus (Diemer and Frobenius 2011), i compiled at Saarland University, Saarbrücken, Germany. This corpus includes 100 blog posts in total from The Times Online. Skype conversations about food between non-native speakers of English are taken from CASE, the Corpus of Academic Spoken English (Diemer et al. forthcoming), ii also compiled at Saarland University, Saarbrücken, Germany. For the purpose of this analysis, a subcorpus of food-related conversations was extracted, containing seven conversations (5.5h) between 14 German and Italian participants in total. The two corpora were examined in terms of contextualisation (reference to place, time, and personal background), register (audience, and linguistic features), as well as personal opinions and lifestyle comments. The paper suggests that both discourse types make use of the multimodal online environment and add evaluative content, for example integrating positively connoted terms to describe food-related issues. The paper also illustrates possible trends towards an increasing inclusion of non-expert language features, such as vagueness and a reduction of presuppositions, and of cultural and personal references, integrating individual background information.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".