Language Shift and Maintenance: A Case Study of Pakistani-American Family
Bibliographic record
Abstract
This article investigated the language shifting and maintenance in daily life conversation of family resided in USA. The sources of data involved the participation of three generations, 7 members total in number, including 4 adults and 3 children. The observation was of observer participant type. The family was observed through video call, continually 10 hours a day in natural environment during 5 days period. Moreover, the interview was continued for 20 minutes, from 4 members of the family, 2 members from 1st generation and 2 members from 2nd. The collected data revealed that the second generation preferred to talk in L2-English but they switched to LI -Punjabi/Urdu when needed. First generation, the older ones talk in LI Punjabi /Urdu. They rarely switched to L2 to facilitate their younger generation mostly they tried to maintain their LI by code switching when interacting with each other even with their young family members. It was a pure qualitative study. Findings suggest that the preference to L2 was to succeed their younger generation in their academic and social life. They consider their social and economic benefits more important than their ethnic linguistic identity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".