Categorie discrete e percezioni continue: per un lessico delle nuove migrazioni
Bibliographic record
Abstract
This article investigates 20 qualitative interviews collected with new migrants of Italian origins settled in Toronto (Ontario) and in London (UK). The aim of the study is to identify the identity markers used by migrants to express their feeling of belonging to Italy and to Canada/UK and to position themselves into two different categories, expat and migrants. According to previous quantitative studies, these two labels refer to two different patterns of immigration: expat in fact includes contemporary skilled and temporary migrations, while migrant deals with unskilled migrations. So, the study of identity markers used in qualitative interviews is crucial in order to investigate how migrants position themselves in the host Country. The results provide evidence of a deep distinction of two different groups of speakers: the first one is composed of those Italians who consider themselves as expats and this is evident since they report in their interviews all those identity markers discussed in the literature as typical of this kind of migration (level of education, social status, use of English). The second one is, instead, composed of those Italians who consider themselves as migrants using those markers already reported in the bibliography for migrants (and not for expats, such as the poor use of English, the low level of education and the temporary job).
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".