Leveraging Technology for Equitable Access to Social Benefits: A Case Study of Civil Society Organization Interventions Among Internal Seasonal Migrant Workers in India
Bibliographic record
Abstract
Internal seasonal migrant workers in India face significant challenges in accessing social security schemes due to their high mobility, inadequate documentation, low digital literacy, regional policy variations, and a general lack of awareness. This paper explores a case study from Jaipur, Rajasthan, where Jan Sahas, a civil society organization and partner within the Migrants Resilience Collaborative, is addressing these challenges using technology. Through the development of the Jan Saathi app, an innovative mobile application, and a scheme discovery and eligibility check platform, this initiative has significantly enhanced service delivery infrastructure for migrant workers. By enabling precise identification of eligibility and facilitating the delivery of benefits, the Migrants Resilience Collaborative has registered over 5.8 million households and successfully delivered over 5.0 million benefits. This approach exemplifies the critical role of leveraging technology in combination with community trust and decentralized implementation, thereby empowering marginalized groups and improving their access to essential services.
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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 | 0.002 | 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 | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".