Introduction: Sociolinguistics and tourism – mobilities, markets, multilingualism
Bibliographic record
Abstract
In the introduction to this special issue on Sociolinguistics and Tourism, we focus on language in tourism as an important window into contemporary forms of economic, political, and social change. Our aim is twofold: (1) to establish and extend ‘sociolinguistics and tourism’ as another social and applied domain of sociolinguistic research; and (2) to use tourism as a lens for a broader discussion of the sociolinguistics of late modernity. To this end, we outline the contours of language and tourism research to date; we consider the (re)conceptualization of key thematics or notions in sociolinguistic research – such as ‘community’, ‘identity’, and ‘language’ itself – as particularly germane to the study of tourism's fleeting encounters; we examine the inevitable tensions between commodification and authenticity; and we explore the links between performances of ‘self’ and ‘other’, and the contestation of different identity positions with regard to social actors’ multilingual repertoires. We illustrate these issues with data examples from several tourist sites, where multilingual resources are deployed for identification, authentication and commodification. Finally, we briefly introduce the papers in this special issue and conclude by commenting on some sociolinguistic consequences of the study of language/s in tourism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".