Securing, Leveraging and Sustaining Power for Street Vendors in India
Bibliographic record
Abstract
While street vendors have provided goods and services to millions at an affordable rate on their doorsteps since time immemorial, erosion of the rural livelihood base, growing informalisation and unabated urbanisation have suddenly increased their numbers in Indian cities in the 1990s. Despite the fact that these workers contribute significantly to the urban economy, they have faced and often continue to experience humiliation, continual harassment, confiscations and sudden evictions. It became imperative to advocate for their rights through the formulation of appropriate policies, the enactment of relevant laws, and the provision of adequate social protection benefits. The National Association of Street Vendors of India (NASVI) played a pivotal and catalytic role in transforming street vendors from non-entities into a formidable force to reckon with. Based on existing published works on the street vendors’ movement in India, a series of key informant interviews and national consultation with stakeholders, the paper aims to document the journey of NASVI in terms of milestones, struggles and successes using the theoretical framework of power resources and capabilities. It also makes an attempt to bring out important lessons for social actors interested in organising informal workers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".