Transformation of international migrants in head wind: Evidence from Tajikistan in the 2010s
Bibliographic record
Abstract
Abstract Tajikistan has relied on remittances by international migrants that have exceeded a quarter of annual GDP to date, despite a series of exogenous adverse events in the mid‐2010s. We use a nationally representative panel dataset collected in 2013 and 2018 to explore the socio‐economic characteristics of households with international migrants and migrants themselves. We provide several new findings. First, the prevalence of households with international migrants is 40%, with a substantial transition in migrant status during the research period. Second, households with international migrants are not poor, have Russian‐speaking members, and are supported by an intense migration network. Third, younger, single, and more educated males comprise most of Tajikistan's international migrants. Fourth, households without Russian‐speaking members or migration networks in 2013 had begun to send international migrants by 2018, while households in the richest group had exited from migration, suggesting s transition of the socio‐economic composition of migrant‐sending households during the mid‐2010s.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".