Roma migration to and from Canada: The Czech, Hungarian and Slovak case
Bibliographic record
Abstract
Most research initiatives on Roma migration focus on Roma migrating from non-EU to EU or EU-to-EU countries. This research aimed to look at another sub-component of the migration process: transatlantic, Canadian migration from the Czech Republic, Hungary and Slovakia. The migration and asylum seeking of Central Eastern European Roma to Canada started in the 1990s when several thousands of Roma moved to Canada. The collection of papers presented here looks at various aspects of Roma migration to and from Canada. Our premise was that 'Canadian Roma migration' should be understood as a process motivated by a mixed set of factors and, from an analytical point of view, it should be studied as neither refugee nor labor migration but as a compound of both. The first two studies in the volume investigate the legal and the political components to the push and pull of Roma migration, while the rest of the papers are based on qualitative, empirical studies that were conducted in three CEE countries – the Czech Republic, Hungary, and Slovakia – as well as in Canada. The country case studies were designed to consider Roma migration from a micro perspective using the same methodology and the same conceptual framework. Researchers in the three countries did fieldwork in villages and towns in which there had been a significant out-migration of Roma, presently or in the past. The factors impelling migration were social, political as well as economic: deteriorating interethnic relations, the rise and spread of violence as well as political racism and fear from racist attacks, deprivation and worsening of living conditions for the poorest, and stigmatized ethnicity, the consequences of which Roma have to face on a daily bases (employment and educational discrimination, verbal and physical racial violence.). As a general pattern, we could distinguish the pioneers from the laggards in the migration process. In all three cases, pioneers were migrants who were from wealthier families and many had earlier migration experiences; they were the ones who would leave, come back, and some would try to leave again. The latter – the laggards – were often the failed migrants, those whose return left them in a more desperate situation than they had been in before leaving.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".