Africa. Migrations Between Perceptions and Data Production in the Long Run
Bibliographic record
Abstract
Africa is a vast continent with more than a billion inhabitants in 54 countries and highly variegated political, economic, climatic and social conditions. Human mobility within a continent that has been the cradle of various cultures dates to prehistoric times. By the mid21st century, Africa’s population will reach two billion and account for almost a quarter of the planet’s inhabitants. The continent will also continue to stand out for the low average age of the population (currently 19 years). Urbanization is increasing, with between 40% and 70% of the population living in cities, depending on the context, while the lack of comparable growth in economic and social resources is leading to a worsening of living conditions, with inevitable repercussions on already intense migratory flows. Forced or voluntary migration is, first of all, internal to the continent. But what are the reasons for emigration? Of the legacies that weigh on the history and present of Africa, the slave trade and colonialism are among the heaviest. This paper reviews the literature on the drivers of African migration, focusing particularly on African perceptions of Europe, and discusses the state of the art in the production of data on migration and its usability in the light of current conceptual and methodological issues.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".