Negotiating Legitimacy: Binational Couples in the Face of Immigration Bureaucracy in Belgium and Italy
Bibliographic record
Abstract
Drawing from ethnographic research conducted with binational heterosexual couples negotiating their legitimacy in the face of immigration bureaucracy in Belgium and Italy, I explore the interplay between marriage migration governmentality and personal subjectivities. In a context of increased political scrutiny, I illustrate how binational couples wield their intimacy to become and stay legal; and how their experiences of the bureaucratic encounters impact on both partners’ agency, producing swinging emotions and improving their legal culture. In Belgium and Italy, marriage to a citizen remains a pathway towards securing residence for the migrant partner. Hence, in both countries these formalities, that I frame as a network of bordering practices, are increasingly – but differently – policed defining divergent marriage migration regimes but similar shared migratory careers for the couples. The potency of the legal-bureaucratic culture fashions the couples’ journey through immigration law and its street-level implementation. Nevertheless, beyond the opportunity structures and nationally anchored constraints, the analysis demonstrates that the partners’ agency similarly emerges from the migration management at large, their personal legal status and biographical resources, and interactions with intermediaries at the margin of immigration bureaucracy. Such agency – triggered by intimate intentions and expectations – is contingent and relational.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".