Convivial linguistic practices: lived togetherness through language in the United Arab Emirates
Bibliographic record
Abstract
Abstract This paper takes up conviviality as an analytical tool to investigate everyday language choices made by foreign residents living in Ras Al Khaimah, a small city in the United Arab Emirates (UAE). It draws on recent work in human geography and cultural studies to understand conviviality in terms of practices rather than outcomes. Specifically, it investigates some of the linguistic dimensions of conviviality deployed by residents of the city in everyday situations of linguistic contact and negotiation of difference. The paper focuses on participants’ “small story” narratives (Georgakopoulou, Alexandra. 2015. Small stories research: Methods – analysis – outreach. In Anna De Fina & Alexandra Georgakopoulou (eds.), The handbook of narrative analysis, 255–272. Malden: John Wiley & Sons) that exemplify everyday language choices in the face of a highly ethnolinguistically diverse as well as racially and economically stratified society. Considering the multitude of ethnolinguistic and socioeconomic divisions in the city and the country as a whole, the paper unpacks how such cross-border contact is negotiated through everyday language practices. The paper identifies four types of convivial linguistic practices described by my participants: language sharing, benevolent interpretation, language checks and respectful language choices. In the process, I also probe the limits of what studying conviviality can tell us about everyday linguistic togetherness in highly segregated societies marked by stark inequalities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".