Identifying the Factors Driving West African Migration
Bibliographic record
Abstract
Since 2014 over 600 000 African migrants have arrived in Italy through the perilous Central Mediterranean route, and nearly 120 000 arrived in 2017. This paper is the first examination of migration motivations at the individual level using nationally representative surveys and focus group data collected in West Africa. Respondents in six West African countries cite economic factors as the reason for migrating and those who wish to stay claim family and love of country as the ties that bind. The study then specifically focuses on Nigeria, the country of origin for a quarter of all Africans traveling through the Central Mediterranean route. Half of the Nigerians were interested in leaving their country of origin if given the opportunity, well above the number in neighbouring countries. Evidence from the six-country survey suggests individuals are migrating for economic reasons but statistical analysis of the Nigeria data reveals a different set of push factors behind the desire to migrate. In fact, economic standing has a limited effect on Nigerians’ desire to leave their home. Instead, individual perceptions of the strength of Nigeria’s democracy are most strongly associated with Nigerians’ desire to migrate abroad, in addition to low levels of trust in local security institutions. Urban and more highly educated Nigerians, especially from Lagos, are also more likely to want to migrate abroad. These findings shed new light on domestic policy steps that could address the grievances and concerns of those who seek to migrate.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".