Policy brief on the impact of narratives in potential migrants' decisions
Bibliographic record
Abstract
This Policy Brief provides an analysis of the narratives of (potential) migrants of Gambian and Afghan origin. This brief focuses on how locally held narratives relate to the messages of EU-funded information campaigns, which usually aim at deterring irregular migration. The research is based on data gathered in interviews and focus groups with Gambians in The Gambia and Afghans in Turkey. The narratives on Europe and migration in both settings are positive, albeit slightly more nuanced and critical in the Gambian context. According to the dominant narratives, there are few or hardly any opportunities in their countries of origin and/or transit. The irregular journey to Europe is dangerous; however, this risk can hardly be avoided given a lack of legal migration opportunities and their current situation. Life in Europe brings opportunities for a positive life change. The migrants’ narratives tend to differ strongly from the messages communicated in the EU-funded information campaigns, with the partial exception of the message emphasizing the dangers of an irregular migration route. Overall, the findings demonstrate that the messages of EU-funded information campaigns often compete with locally held narratives on migration and Europe. When the messages of information campaigns appear irrelevant or at odds with the life conditions of (potential) migrants, they tend to be discarded in favor of local narratives that better express these realities. As a matter of fact, it is relevant not to overestimate the capacity of migration information campaigns and carefully reflect upon their objectives.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.007 |
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".