Nurturing Sociolinguistic Competence in Pakistani ELT Context through Teachers: Practices, Perspectives, and Practicalities
Bibliographic record
Abstract
Sociolinguistic competence or the appropriate use of language in different social scenarios is a construct that depends on such social variables as age, gender, class, and ethnicity as well as on different socioeconomic categories too. Teachers have a vital role to play in developing both receptive as well as productive sociolinguistic competence in any society, especially where English is a second language. The present research aimed at investigating public sector school teachers’ perceptions and practices regarding sociolinguistic competence in Pakistan. In terms of sociolinguistic competence, the study has a special focus on context-appropriate grammar and vocabulary usage. The study consisted of two phases, in the first phase perceptions and practices of school teachers were analyzed using the works of Jianda (2006), Abedi (2016), and Blum-Kulka & Olshtain (1981). In the second phase, building upon the findings, various training sessions were conducted in the Twin Cities of Pakistan to identify the hurdles and suggest practical solutions for the development of sociolinguistic competence among English language learners. The findings indicated that the unawareness of English culture is the major reason for cultural instruction failure in English language learning lessons. Also, by using feasible and cost-effective measures we can develop the sociolinguistic competence of English language learners at public sector schools.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 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".