From Waste to Inclusive Growth: A Digital Platform for “Ragpickers” in India
Bibliographic record
Abstract
Inclusive growth ensures that economic growth directly serves the welfare of lower income and excluded groups of society while focusing on profitability and financial success. Inclusive growth research in management and innovation studies has been supported by the UN, WEF, OECD and World Bank. Digital technologies, digital innovation, and digital entrepreneurship can be critical drivers of growth, productivity, and ultimately, poverty alleviation among marginalized and excluded communities. Yet, there are very few IS studies that utilize an inclusive growth lens and examine the role that digital platforms can play. This paper addresses this gap and adopts a single, revelatory case study approach to explain the process of creation and management of a platform-driven ecosystem and development of capabilities for inclusion of a community of waste collectors in India. The paper presents a process framework and contributes to both inclusive growth and platforms literature while also providing practitioner insights.
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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 | 0.001 | 0.001 |
| 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 | 0.004 |
| 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".