Temporary migration: category of analysis or category of practice?
Bibliographic record
Abstract
Temporariness has become an increasingly salient feature in international migration that presents itself as fragmented, non-linear, including different intermediate stops and multiple returns and new departures. This special issue proposes a new analytical framework that brings together the role of policies defining migrants as temporary and the role of migrant’s own agency in perceiving their migration project as temporary or permanent. The proposed analytical framework covers both low- and high-skilled, legal and irregular migratory flows, and different visa and citizenship regimes. This introduction starts by discussing the relationship between migration and time pointing to its multiple facets. The second section discusses temporary migration as a policy category looking at how it is regulated in more or less flexible regimes, including categories of temporary migrants that are not usually included in temporary migration debates, notably international students or working holiday makers. Section three turns to the lived experiences of migrants and the ways in which they conceptualise their migration (or their migration plans) as temporary or more long term, emphasising how these views can be also changing over time and through the actual migration experience. The final section brings the two strands together and presents the contents of this special issue.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".