An Analysis of ‘Migrant-intensity’ in India and Indonesia
Bibliographic record
Abstract
Emerging economies are witnessing the large-scale movement of internal migrants. While the popular discourse on internal migration imagines migrants from villages flooding into the large metropolis, scholarship is increasingly emphasizing the existence of multiple migration pathways, as well as the emergence of more dispersed patterns of urbanization. To root these discussions in particular geographies, this paper introduces the concept of ‘migrant-intensity’ as an empirical way of understanding the places that experience migration in the most profound and transformative ways—where the challenges and opportunities inherent in transience and mobility are most apparent. Analyzing census data from India and Indonesia, we show that ‘migrant-intensity’—a measure of in- and out-migrant concentration—is highest in a diverse set of non-metropolitan spaces, including secondary and tertiary cities and ‘rurban’ geographies. We argue that migrant-intensity as an empirical tool can advance scholarship on complex migration patterns by identifying the places at the crossroads of migrant pathways. Moreover, it can help planners and policymakers to address unique challenges, opportunities and constraints of migrant-intensive places.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".