A Corpus Approach to Discourse Analysis of Newspaper Restaurant Reviews: A Preliminary Analysis
Bibliographic record
Abstract
This study is a corpus-based discourse analysis that explores specific discourse communities of restaurant reviews in newspapers. The design of this study is largely influenced by the works of Swales (1990), Bhatia (1993), and Biber et al. (2007), and is guided by understanding how a professional text in a particular discipline achieves its disciplinary objectives. A specialized corpus was constructed and the data were randomly selected from restaurant reviews from five leading newspapers in US in 2010. The analyses focused on the distributions and functions of surface linguistic features including move analyses, analyses of communicative purposes in the texts, and investigations of the vocabulary and typical lexico-grammatical realizations of these moves. The results have shown that the establishment of the dining experience (Move 3) (46.3%) occurred most frequently, followed by a description of the entering (Move 2) (22.0%), and then a detailed description of the chosen restaurant (Move 1) (14.7%). Most reviews were structured chronologically and were similarly arranged in the following order: experience of choice, entering, dining, paying, and consideration of another visit. In addition, some rhetorical signals were noticed. The implications of the findings are presented with possible suggestions for future teaching and research issues. Key words: Corpus; Discourse analysis; Restaurant review
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".