‘In‐betweeners’ in turbulent times: Migrants in the epicentre of diverse ‘crises’ in the Americas and Europe
Bibliographic record
Abstract
This Special Issue (SI) brings together a selection of articles connected by the interplay of two main concepts or ideas.The first one refers to the fact that we are living through increasingly unstable and challenging times, defined by a succession of 'turbulences' that are often characterized as overlapping and interconnected 'crises' -a so-called era of 'polycrisis' with implications in multiple domains (Henig & Knight, 2023).In this global context, diverse phenomena interpreted as crises can bring about or impede migration, trigger the implementation of certain policies, and have an impact on migrant experiences and strategies.Existing work has focused on the links between migration and global crises, for instance, in response to the impact of the 2008 Great Recession (see Tilly, 2011, for an international perspective; Lafleur & Stanek, 2017, for the European context; Sassone & Yépez del Castillo, 2014, regarding migration between Latin America and Europe).However, the situation has evolved since as other turbulences occurred, including the 2015 'refugee crisis,' the COVID-19 pandemic, and the Venezuelan humanitarian and migration 'crisis.' Aware that turbulences are not a new phenomenon at the international level but a key concept in assessing major transformations (Rosenau, 1990), our SI highlights the speed and broad repercussions of recent changes in which people on the move appear in a central position.The second concept relates to the figurative notion of migrants as 'in-betweeners' -i.e.occupying intermediate or diffuse positions not just in space and time (Crawley & Jones, 2021;Gius, 2021) but also in terms of migration categories, political practices, mobility strategies, and family relations.The articles in this SI explore how migrants as in-betweeners often find themselves at the epicentre of turbulent scenarios, portrayed alternatively as threats, victims, scapegoats, or agents.In this respect, our main goal is to investigate the (re)definition of migrants' positions and roles in such scenarios and how their strategies intersect with policy frameworks.To do so, we focus on the intersections between policy and agency at local, national, regional, and transnational realms,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".