Code-Switching and Identity: A Sociolinguistic Study of Hanif’s Novel Our Lady of Alice Bhatti
Bibliographic record
Abstract
The present research explores the features of code-switching in Hanif’s (2011) English fictional novel Our Lady of Alice Bhatti. The research explores code-switching by applying the Whorfian Hypothesis (Linguistic Relativity and Determinism) through textual analysis of Hanif’s novel (2011). One of the distinguishing features of the novel is the use of two distinct languages (English and Urdu) which majorly cause the implementation of various features of code-switching and code-mixing. The researchers have employed the qualitative research approach during data analysis. The study examines how language influences ideas and identity with the use of code-switching. A comprehensive study or analysis of the relevant literature has also presented in a comprehensive way leading towards a theoretical framework of preferred Whorfian Hypothesis (Sapir-Whorf Hypothesis) in the field of sociolinguistics. The results and findings of the also proves that the writer of the novel consciously/unconsciously utilizes the technique of code-switching of code-mixing to highlight/promote the native/local identity (ies) and cultural values through the code-mixed language. The study would be helpful for the reader to develop an appropriate understanding of code-switching in language varieties.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".