Bibliographic record
Abstract
Over the past years much attention has been placed on the ordeal of migrants as they leave their home countries and seek refuge or better lives in others. Given the sudden surge of Bangladeshi migration to Italy in recent years, this article focuses on Bangladeshi migrants in Italy and examines the precarity that they face or have faced. Our analysis is based on observations gleaned from the existing literature and our own field study of 18 Bangladeshi migrants in two adjacent regions in Italy. We look at the precarity faced by Bangladeshi migrants (1) pre-migration in Bangladesh, (2) during migration from Bangladesh as they passed through different countries, and (3) in their current host country, Italy. Precarity can have different but often overlapping meanings, for example, “labor precarity”, “life precarity”, and “place/legal precarity”, among others. We have used these different lenses of precarity to examine the experience of Bangladeshi migrants of Italy. The existing literature on Bangladeshi migrants does not use a precarity lens explicitly, nor does it consider the experience of the migrants in all three of the above stages of their migration together. We conclude that generally these Bangladeshi migrants face precarity in its various forms, in all stages of their journey, and in many spheres of life in their current host country. Recognizing the precarious nature of the existence of many of the Bangladeshi migrants is very important in any discussion of migrant issues that their host country, Italy, is facing.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".