Bibliographic record
Abstract
Refugees and other distinct migrant populations often travel together. The policy concept of 'mixed migration' arose to describe this migration phenomenon. There are, however, various meanings of the term. These can be divided into two categories: on the one hand are understandings that focus on the complex composition of migration flows; on the other are understandings that consider complexity and individuals' mixed motivations for moving. Because of this, 'mixed migration' has contributed less to thinking around, and humanitarian action in relation to, migration than it might otherwise have. This paper describes these diverse understandings and harnesses relevant legal principles drawn from refugee, human rights, humanitarian and transnational criminal law, as well as from the law of the sea in support of one understanding of the term. It argues that international law augers in favour of an understanding focused solely on complexity, because the legal principles applicable in mixed migration situations apply regardless of individual motivations. Including such motivations within the policy concept only serves to divorce 'mixed migration' from its legal underpinnings. Moreover, understanding 'mixed migration' in terms of varied individual motivations for moving might fuel populist anti-immigration sentiment. A complexity-focused understanding of mixed migration would enhance the concept's utility.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.063 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| 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".