ESP Course Evaluation of Business Communication for Masters in Commerce of a Pakistani University
Bibliographic record
Abstract
English is the Language of globally expanding world of trade and commerce. English for Specific Purposes (ESP) is time-honored area of English Language Teaching (ELT), and it is by a long way at variance from General English (GE) because of its practical efficacy and learner centered approach. The need of ESP (Business Communication) at post-graduate colleges of commerce and departments of commerce of universities at master’s level classes is to outfit the students with most recent communicative trends so that they can become resourceful members of some reputed business house. In the present study the researchers have done ESP course evaluation of the current course of Business Communication for Masters in Commerce classes of Bahauddin Zakariya University, Multan. This course is taught at public and private sector post-graduate colleges of commerce affiliated with the university in the first year of two year master’s degree program. For this purpose the researchers have used questionnaire for the students and interview for the teachers. After analysis of results, the current study presents the conclusion that the current course of Business Communication is unable to fulfill all the professional needs of the students and is also unable to link theory with practice. It needs improvement having in view the modern tendency in the field of Business Communication, the needs of the future experts and the requirements of national and international business concerns.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".