Attitudes Towards English & punjabi Language Learning in Faisalabad
Bibliographic record
Abstract
Pakistan being a linguistically diversed region has a diglossic situation in which two or more distinct languages can be used by the same speech community. However, these languages differ in status, prestige and function, which entitled them highly prestigious ( H )language and less prestigious language ( L) languages. We have English as highly prestigious and Punjabi local vernacular informal language. So, present study aims to identify attitudes towards English which is a sophisticated, official, formal, as well as language of education, science, heritage and towards Punjabi which is local, vernacular, broken, language as well as language of illiterate community. This situation is surprising that English which has no native speaker has marginalized all local languages whereas; Punjabi with a large no of native speakers is socially neglected and sidelined language. This study was based on the hypothesis that there are different attitudes towards English and Punjabi language learning. In order to know the attitudes towards English and Punjabi languages close ended questionnaire has been used as a tool to collect the data collected, from 42 students of 8 different educational institutes: government, private, madrasa of Faisalabad. The whole data was statistically analyzed and frequencies were calculated for each item. This study concludes that people of Faisalabad have more positive attitudes towards English than Punjabi language because they differ in status, structure, function, and prestige. This study is significant because it highlights the economical, educational, social status of Punjabi and English languages in Faisalabad.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".