Migration Causes and Challenges at the European Border in North Africa: A Practitioner-Based Grounded Theory
Bibliographic record
Abstract
This investigation took place in Melilla, a European city in North Africa, and in the neighboring cities of Granada and Malaga, on the southern Mediterranean coast of Spain. As well as exploring an under-researched geographical area, the article also focuses on an under-researched group in the field of migration: first-line practitioners. Grounded theory research was conducted to develop a working model of practitioners’ perspectives surrounding causes of migration, and related challenges, at (and through) the European Union-Moroccan border. Semi-structured interviews and focus groups were used to gather qualitative data from 19 first-line practitioners. Their perspectives suggested a complex set of causes underlying migration at an economic, individual, community, national, and geopolitical level. They also identified eight central risks, and challenges, for the migrant population during their journey to (and process of establishing their lives in) Europe. To reduce such risks, practitioners emphasized the need for safe migratory routes, quick institutional responses in the presence of unexpected phenomena (such as COVID-19), promoting and educating for empathy, and the development of more interregional solidarity on a national, European and international level. The paper concludes reflecting on the importance of these proposed responses in “out-of-place” European territories such as the city of Melilla.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".