Migration intention and Indigenous social networks in the place of origin: a socio-centric network analysis in the rural western highlands of Guatemala
Bibliographic record
Abstract
The rural highlands of San Marcos are among the highest-volume migrant-sending departments in Guatemala. This is the first study to explore the social network dynamics predictive of emigration from an Indigenous Maya community in Guatemala to the United States. It utilizes sociocentric network data in place of origin to assess drivers of migration. Using sociocentric census data of a single village (N = 653), we assessed whether close social ties with people who have already migrated were associated with an individual’s plans to migrate within the year. We also explored network factors that influence the decision to emigrate or remain in place. Our findings show that, controlling for remittances and demographic characteristics, having an emigrant tie in the United States alone was not predictive of plans to emigrate. Those with a close friend or neighbor who already emigrated were more likely to emigrate themselves. In terms of network variables, those with a higher number of social ties in the village were less likely to emigrate, and individuals more central in a network were significantly less likely to want to emigrate. This study may provide insight into factors driving sustained out-migration or influencing staying in place in rural Guatemala.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".