On the Status of English as a “Lingua Franca”: An EFL Academic Context
Bibliographic record
Abstract
Recent trends prevalent in the studies about language are stressing the vital role culture, as an inseparable part of language, plays in accumulating so-called international or worldwide prestige. English has become the official language of the business and scientific world (Schutz, 2005). There are a number of elements backing and contributing to this process, directly or indirectly. These elements are mostly of political nature. This study tries to examine and question the present worldview of an EFL academic context regarding the status of English. It is carried out at two interwoven levels. The first section is devoted to the current status of English as the world's number one language of science, politics, sports, business and the like. To this end, a brief, but concise historical overview of “English as a lingua franca” or “English as an international language” and the leading scholars viewpoints in this regard, is provided. The next section is an attempt to crystalize the factors that have helped or are helping this language to gain and develop such a worldview. This qualitative study is pursued through triangulated data collection procedures in an EFL academic context. The data required for this study was gathered through observations, semi-structured interviews, field notes, and focus group discussions. The results signified that English has found its way and position as the world's most recognized lingua franca and this is a policy which is left implicit and untouched in many contexts, such as the Iranian EFL context. Key words: English as a lingua franca; English in academic context; Critical thinking
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".