Who Matters Most? Migrant Networks, Tie Strength, and First Rural–Urban Migration to Dakar
Bibliographic record
Abstract
Social networks' influence on migration has long been explored largely through the lenses of cumulative causation and social capital theory. This article aims to reconceptualize elements of these theories for the case of rural-urban migration and test their utility in explaining first-migration timing. We use a uniquely extensive social network survey linked to prospectively collected migration data in rural Senegal. We decompose migrant networks into return migrants, current migrants, and nonmigrant residents of the destination to capture heterogeneity in migration-relevant social capital. As expected, the number of nonmigrant alters living in the capital, Dakar, has an outsized association with the migration hazard, the number of current migrants from the village living in Dakar has a smaller association, and the number of return migrants has little association. Drawing on social capital theory, we test the influence of (1) subjectively assessed tie strength between the ego and their network alters and (2) structurally weak ties measured through second-order ("friend of a friend") connections. Weak and strong subjective ties to current migrants and nonmigrant Dakar residents are positively associated with the first-migration hazard. Structurally weak ties to current migrants are too, but only for individuals with no direct ties to current migrants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".