Bibliographic record
Abstract
This paper aims to analyse the verbal techniques which are more frequently used in tourism discourse, that is to say comparison, key words and keying, testimony, languaging, and ego-targeting (Dann, 1996). In order to do that, five official websites have been chosen for analysis, namely the websites which promote the USA, Canada, Australia, Great Britain, and Italy as tourist destinations. The linguistic content available on these websites has been downloaded and five comparable corpora have been assembled and analysed through WordSmith Tool 6.0 software for linguistic analysis (Scott, 2012). The methodological approach adopted combines the Corpus Linguistics approach with Cross-cultural studies models, in order to extract quantitative data and to interpret them from a linguistic and cultural perspective (Manca, 2016a). The aim of these analyses is to show that, although these techniques are all peculiar of tourism discourse, they are employed with different frequencies by the five languages/cultures with relevant implications for cross-cultural tourist communication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".