Introduction: Resisting Anti-Migrant Politics: Challenging Borders, Boundaries, and Belongings in Europe and Africa
Bibliographic record
Abstract
This special issue argues that the novelty of current migration realities is not so much due to the scale or forms of migration practices as it is to as the rise of anti-migrant politics, which has led to the institution and differentiation of novel border regimes. Over the years, practices of resistance have developed against these regimes and these politics in different places and on various scales. This special issue highlights the emergent interplay of anti-migrant politics and everyday practices of resisting and subverting them. In their combination, the four articles in this issue make two important contributions: they address the increasing need to unveil unexpected forms of challenging dominant regimes of borders, boundaries, and belongings, and they present a specific case-study-based methodological perspective for capturing counterintuitive and unexpected forms of resisting anti-migrant politics. This special issue stresses the importance of studying resistant practices in different local, regional, national, and continental settings in a comparative and longitudinal manner. Additionally, it emphasizes the consideration of the role of anti-migrant politics and practices as they relate to resistant practices in countries of departure, as in geographic contexts such as the African continent, even if – and especially when – attempts of migration fail due to enhanced border control.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".