An Ethnographic Investigation of Code Switching and Mixing in Pakistan: A Case Study of Nine-Year Old Child, Alia
Bibliographic record
Abstract
Bilingualism has special designation in learning of language especially in countries where English is second or third language. Bilingualism sketches the concept of a speaker that either mixes or switches two or different codes while making an utterance. In that perspective, the present study is designed as an ethnographic research to validate the notion of bilingual action of speech turning in Pakistan. This case study of nine year old child named Alia has been conducted to formulate the situation of bilingualism in multilingual society of Pakistan. In that regard the researcher recorded the dialogues of a child while taking part in different roles. The speech of the child was later analyzed to investigate the influence of bilingualism on it. The two languages were focused Urdu and English with the little extent of Punjabi code. Various reasons were researched for finding out the basic factors that led to switch or mix codes within an utterance. The parents of a child Alia were also interviewed that led to find specific factors of their child to code switch and mixes. The factors include bilingual environment, partners, mother tongue, social interactions and medium of education in school.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".