Uso e forme dell’inglese come marcatore identitario tra expat e migranti
Bibliographic record
Abstract
According to the few studies on new migration, migrants who migrated in the last two decades usually consider themselves different from those who had migrated in the Fifties or the Sixties. They highlight this perceived difference through their ability to keep the varieties of their linguistic repertoire (i. e. Italian and English and/or the language of the host country) separated. Although there is a growing body of studies on this topic, further surveys are needed to better understand the specific linguistic behaviour of expats who perceive their experience to be different from that of recent migrants who do not identify with the category of expat. Therefore, this paper aims to compare two groups of migrants: the members of the first one who do not perceive themselves as expats but as 'migrants' or 'Italians abroad'; and a second group of Italians who consider themselves as 'expats'. The analysis focused on code-switching to English by expats and non-expats settled in two Anglophone cities, Toronto and London. The aim was to verify if there is variation among these two groups and if expats more frequently use English than the migrants who refuse to be included in the expat category. In doing so, we discussed the role of the use of English as identity marker by expats, adopting both a quantitative and a qualitative approach.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".