Delivery of social welfare entitlements in India : unpacking exclusion, grievance redress, and the role of civil society organisations
Bibliographic record
Abstract
The COVID-19 public health crisis and subsequent containment measures followed in India have severely impacted poor and vulnerable populations with respect to food security, livelihood, and access to health services. The national lockdown led to significant distress among citizens due to employment loss, wage cuts, transportation etc., and increased dependency of people on social protection schemes. Although several relief measures have been mobilised by the government, there has been extensive documentation of exclusion of deserving people from availing these social protection measures. In this research project, Gram Vaani, Dvara Research, University of Montreal and Tika Vaani utilised their collective knowledge and field resources to undertake action research specific to the context of the COVID-19 pandemic. The report explores three research objectives: 1. Analysis of over 20,000 voice reports of grievances submitted on one of Gram Vaani’s Interactive Voice Response (IVR) platform to understand the different challenges citizens face in accessing social welfare entitlements. 2. Understanding the various modalities through which Gram Vaani volunteers assist callers in resolving the hindrances they report. 3. Proposing a set of Standardised Operating Procedures (SOPs) that can be used by civil society organisations to reduce exclusion at the last mile.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".