Migrants' communication with local people in the receiving country : experiences and approaches in Malta
Bibliographic record
Abstract
A small island country in the Mediterranean, Malta ranks among the most densely populated EU countries. The fact that Malta is a strategic location between Africa and mainland Europe and that nearly a quarter of its population are migrants gives it a notable position in migration studies. Although interdisciplinarity is inevitable in migration studies, the field of communication is often neglected. The primary purpose of this ethnographic study is to explore how migrants' experiences shape their communication approaches with local people and the receiving society in Malta. To examine this, thirty-five migrants were interviewed, and participant observation was conducted over a period of fifteen months in 2023-2024. Based on the results, migrants are categorised into four groups according to their varying interaction experiences with the local people: hesitant migrants, selective migrants, migrants with positive outlooks, and neutrals. Notably, non-EU migrants who are exposed to ethnic discrimination and discriminatory social media content have a negative approach to communication with local people. In addition, socio-economic conditions play a decisive role in the communication processes of migrants, and proficiency in Maltese is seen as one of the keys to overcoming bureaucratic obstacles for migrants rather than achieving social inclusion. In light of the results, the study demonstrates the need for policymakers, NGOs, and all relevant stakeholders to develop inclusive communication strategies that strengthen relationships between migrants and local people.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".