International Mobility and the Multilingual Identity: A Sociolinguistic Analysis of Study Abroad Experiences
Bibliographic record
Abstract
Pakistani students studying in diverse global settings actively shape their multilingual identities in response to varying sociolinguistic contexts. The present research employed an Identity and Investment Theory proposed by Darvin and Norton (2015), utilizing semi-structured interviews with five PhD students studying abroad in China, Italy, the UK, Canada, and Sweden, who were selected by using a purposive sampling technique. The results suggest that the students made deliberate choices in their language use, prioritizing English and their mother tongue, Urdu, due to the emotional, social, and academic benefits associated with each. At the same time, local languages such as Mandarin and Italian were also incorporated for social integration and day-to-day interactions. Linguistic choices are often influenced by broader ideological constructs, including the perceived prestige of certain accents and the desire to achieve native-like fluency. Emotional challenges are significant, which is why students often feel tired and insecure. Overall, the research emphasized the need for educational systems to recognize the emotional and cultural dimensions of language use and to support students from diverse linguistic backgrounds in more impactful and inclusive ways.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".