Bibliographic record
Abstract
The focus on returns at the EU-level intensified in the last year, with EUropean policymakers asserting that less than a quarter of those who are issued with a return decision return to their country of origin. This commentary investigates this ostensible ‘return conundrum’, examining both its framing and the interpretation of the data that it rests on. It uses this term to refer to the low level of returns carried out compared to the number of return decisions issued and detections of ‘illegal’ border crossings at the external borders. By analyzing Frontex’s statistics on returns from 2010-2024, I argue that the evidence base that this conundrum springs from is at least partially exaggerated since the reports potentially double count return decisions issued and non-counts all voluntary returns. This is because Frontex counts the number of return decisions issued rather than the number of people who are subject to a return decision, which inflates the statistics since multiple return decisions might be issued to the same person. On the other hand, until recently not all member states systematically monitored voluntary returns, which means that they have been undercounted.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".