The influence of L2 on L1: metapragmatic judgments of L1 non-verbal greetings by Saudi L2 speakers of English - a mixed methods study
Bibliographic record
Abstract
This study investigates the influence of English L2 on metapragmatic judgments of Arabic L1 non-verbal greetings behaviours. Through a sequential mixed methods approach, it looks at the effect of length of residence in the L2 target culture, cultural orientation, and personality traits on metapragmatic judgments of L1 non-verbal greetings by Saudi residents in the UK. The participants are 437 Saudi and British adults, made up of three groups: 1) Saudis with experience of living in the UK with English as their L2; 2) Saudis in Saudi Arabia who had never lived in the UK with English as their L2; and 3) British L1 English speakers living in the UK who had never been to Saudi Arabia. The data was collected using an online questionnaire and semi-structured interviews. The online survey consisted of scales on appropriateness of non-verbal greeting behaviours displayed in four social relational situations, the Vancouver Index of Acculturation, and the Multicultural Personality Questionnaire. This quantitative data was complemented by the qualitative data collected through semi-structured interviews with nine UK-based Saudi adults. There was variation found between the three groups in their metapragmatic judgments of Saudi non-verbal greetings. Moreover, attachment to L1 Saudi culture was positively linked with UK-based Saudis’ metapragmatic judgments of L1 non-verbal greetings, whereas acceptance of L2 British culture negatively affected their judgments of L1 non-verbal greeting behaviours. Amongst UK-based Saudis, Cultural Empathy and Openmindedness were both strongly related to appropriateness ratings of various L1 non-verbal greetings behaviours. There was also a link with levels of Social Initiative, and Flexibility. This suggests that a person’s L2 influences their metapragmatic awareness of their L1, confirming the principle of multi-competence of L2 users (Cook, 1992, 2003).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".