Investigating the Social Functions of Code-Switching in Amarbail by Umera Ahmed
Bibliographic record
Abstract
The present study attempts to investigate the social functions performed by English code-switching in an Urdu novel, Amarbail, written by Umera Ahmad. The focus of the study is to examine how the societal norms and patterns of a contextual society are externalized in the selected novel through code-switching phenomenon. The study has a pragmatic stylistic focus and qualitative method has been utilized to describe the social functions of code-switching in the selected novel. The data have been selected by employing handpick non-random sampling and insights have been taken mainly from Myers-Scotton, and Albakray and Hancock for the analysis of data. The results reveal that the code-switching is a deliberate literary device exploited by the author to show the sociolinguistic norms of the target society. The main social functions performed by English code-switching in Amarbail are i) depiction of power relations, ii) construction of fluid identities, iii) acculturation and iv) maintaining class difference. The study concludes that code-switching in a literary text is a multipurpose phenomenon which not only symbolizes the societal norms of its contextual society but also displays the writer’s artistic and creative aptitude. It is hoped that the bilingual writers and researchers would find results of this analysis opportune and beneficial.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".