Searching for Fortune: The Geographical Process of Nigerian Migration to Dublin, Ireland
Bibliographic record
Abstract
The migration of Nigerians to different parts of the world is not new. From Greenland to Kabul, there are resident Nigerians. [1] However, Nigerian migrants move predominantly to the countries where they are more likely to adjust rapidly in terms of being able to understand the host country's language, to secure gainful employment, and to reunite with members of their family, friends or associate with other people from their country of origin. For these reasons, the United Kingdom, United States and Canada are some of the most popular destinations for Nigerian migrants. This paper conceptualizes the contemporary migration of Nigerians as an incessant quest, as it were, the departure of mostly well educated and young people from their home country to more fertile pastures abroad, in search of their individual fortunes. The process of migration creates Diaspora in host countries like Ireland that create significant changes in the lives of the migrants as well as the social and economic geography of the host country. In some ways, the migration can also be conceptualized as an Israelites' journey, a journey that takes Nigerian migrants from one promising foreign country to the next until they find the proverbial promised land.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".