The Perceptions of Arabic-Speaking Jordanian EFL Learners about Multiculturalism and Multilingualism
Bibliographic record
Abstract
This study presents quantitative research on the opinions of Jordanian EFL learners regarding multiculturalism and multilingualism. It evaluates how learning English is linked with the cultural association of these learners. The communicative competence model, which explains multilingualism and multiculturalism as being associated with the different competencies of students in acquiring a language, is applied. It applies a survey methodology to investigate the perceptions of Jordanian EFL students regarding their multilingualism and multiculturalism. A sample of 426 undergraduate students was used in the analysis. The main findings showed that the students prefer collaborative learning strategies. It also showed that the students are more interested in standard English and have a relatively low cultural affiliation with native English-speaking countries such as the United States and Britain. Jordanian students seem to have relatively low levels of multiculturalism, although they consider themselves citizens of the world in seeking to learn English as the language of globalization. Multilingualism for Jordanian EFL students seems to focus mostly on standard English.
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) | 0.000 | 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) | 0.002 | 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".