Bibliographic record
Abstract
In a globalized world of constantly challenging demands, the acquisition of English as a lingua franca remains essential. Bearing this in mind, the current paper explores how sustainability in English language acquisition can be emphasized to ensure quality outcomes in the long run. The study, as such, raises two questions: what components should an English as a foreign language (EFL) university course comprise to ensure sustainability in English language acquisition, and what are the specifications of an EFL course, designed to ensure sustainability in English language acquisition? Employing a mixed-methods design, the author of this paper distributed a questionnaire and conducted an interview covering two categories of subjects; namely, EFL instructors and university students. The study findings stress the need to integrate literature, life skills, Sustainable Development Goals (SDGs), communication skills, environment-awareness education, and human values into English language courses. In addition, the findings highlight several specifications for an EFL course designed to ensure sustainability in English language acquisition, such as utilizing technological advancements, tailoring English courses as per current trends, focusing on students’ research projects, encouraging community involvement, conducting co-curricular activities, and implementing Knowledge-Skill-Disposition (KSD)-oriented assessments. The study ends with a proposed framework for scholars to consider.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".