On the deportation charter: using freedom of information research to map the UK’s charter flight operations, 2010–2024
Bibliographic record
Abstract
Like the detention centre, the deportation charter flight is integral to the way states in the global North conduct deportation. Unlike the detention centre, the charter flight system has received little scholarly attention in its own right. Taking the UK experience as our case study, and combining Freedom of Information research with the analysis of official documents, we examine the deportation charter flight system in quantitative and qualitative depth. We make three contributions. First, we provide a statistical overview of all flights from 2010-2024. Since the UK publishes little systematic information about charter flights, this statistical overview addresses a significant gap in our knowledge of the landscape of immigration enforcement. Second, we show how charter flight operations create deportation routes and we map the changing geography of these routes. In essence, we reveal that it is not only migrants and their facilitators who create migration routes; states do too. Third, we focus on migrants’ resistance to deportation on charter flights which results in many empty seats when a plane takes off. We show that some routes are less effective than others at filling planes and carrying out deportations. Reasons for this are unclear; we offer some hypotheses for further research.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".