Responsible innovation with digital platforms: Cases in India and Canada
Bibliographic record
Abstract
Abstract Marginalized communities globally encounter grand challenges such as lack of access to education, healthcare, and sustained livelihoods. Several initiatives to address these complex, global problems have resulted in fragmented solutions. Recognizing this, there have been several calls for the study of responsible innovation (RI) to address grand challenges. Digital platforms such as AirBnB, Uber and so forth have now become commonplace and are known to generate economic value but also face criticism for being exploitative and exclusive. Only a handful of studies show how similar platforms can innovate responsibly to serve marginalized communities by generating simultaneous economic and social value. To address this gap, our study examines the cases of two platforms that orchestrated ecosystems consisting of individuals from marginalized communities, government agencies, and other entities to provide physical, digital and societal solutions based on principles of RI. We contribute to the RI and IS literatures to show how RI solutions can be fostered through digital platforms to address grand challenges. The article provides empirical evidence of all four dimensions of the RI framework—anticipation, reflexivity, inclusion, and responsiveness ‐ and their operationalization through digital platforms. This research lays the foundation for future studies at the intersection of RI and digital platforms literature. The study also provides practice insights on developing digital platform solutions for marginalized communities to address grand challenges and is useful to policymakers to formulate appropriate interventions. It pushes the theoretical and practice boundaries of our understanding of RI and digital platforms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".