English as a lingua franca – a native-culture-free code? Language of communication vs. language of identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
English has become the dominant means of international communication. Its non-native speakers now far outnumber the conventional native speakers in the UK, the USA, Canada etc. Against this background, a number of authors have recently stressed the functions for which foreign languages are learned. They make a distinction between a ‘language of communication’ and a ‘language of identification’. The terms, which were coined by the German applied linguist Werner Hüllen (1992), have recently been popularised in the context of English as a lingua franca. English, it is said, can be used as a language of communication without necessarily being a language of identification. As it is used for practical communicative purposes, correctness and particular stylistic features associated with the speech community from which it originates are of lesser importance. Recent developments in European language policy seem to be focused in the same direction with the proposal that the EU should advocate the idea of a “personal adoptive language”. This language should be freely chosen by every European and it should be “different from his or her language of identity, and also different from his or her language of international communication” (Maalouf 2008). The paper examines the use of the terms ‘language of communication’ and ‘language of identification’ in the literature and challenges the existence of the dichotomy with regard to the English language as it is used today. Focusing on phraseology (i.e. idiomatic phrases and pre-fabricated speech), the article shows a number of language practices that are used by non-native speakers of English to display identity.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it