Linguacultural Representations in Specialized Migration Discourse: A Lexicographic Perspective
Bibliographic record
Abstract
In the last two decades increasing movements of people across countries, due to economic and social reasons, have produced high levels of exchanges among speakers of different languages where English is used globally as a lingua-franca (ELF). Moreover, migration flows across nation states (especially from non-Western countries to Western ones) have encouraged the movement of people, mainly of African and Asian origin, from students to skilled workers, who are often involved in English-mediated interactions where migrants’ native linguacultural background inevitably connects to the language spoken by the host community (e.g., in European countries), and at the same time shapes the use of English as a global means of interaction (Canagarajah, 2013). ELF cross-cultural interactions and translingual processes, naturally occurring in intercultural settings, are particularly remarkable, since they plainly show how ELF speakers, engaged in intercultural interactions, differently appropriate the English language, according to their own native linguacultural patterns, and to specific pragmalinguistic purposes and processes (Guido, 2012, 2018; Mauranen, 2018). This study will address the influence that lexical actualizations in authentic spoken encounters, as well as in written productions in specialized contexts have on the current role and function of English as an international language and which deserve coverage and consideration in lexicographic resources. Research studies on migration narratives, language mediation, cross-cultural conceptual representation and reception of traumatic events, where ELF lexical processes are often activated by the speakers involved, are particularly important to address the development of linguacultural representations that should be covered in dictionaries, lexicons and other lexicographic resources, especially online.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".