A moral economy of suspicion: Love and marriage migration management practices in the United Kingdom
Bibliographic record
Abstract
This article investigates how marriage migration management practices in the United Kingdom (UK) have entered the realm of security policy by relying on a moral political economy of suspicion that notably mobilizes what I call ‘technologies of love.’ The latter refers to conceptions of ‘true love’ and its manifestations that infuse the regulation of belonging through immigration controls. I argue that this economy of suspicion builds on and partakes in a governmental regulation that result in a stratification of rights. After outlining how Western romantic love has a history that cannot be uncoupled from state concerns about race and citizenship, I detail how Didier Fassin’s notion of moral economy of suspicion relates to technologies of love in the governmentality of marriage migration. Finally, I examine recent legislation and policing practices in the UK to illustrate how technologies of love can become part of judgment formation in evaluating possibly suspicious couples, notably European citizens marrying non-European citizens.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".