Responsible innovation with digital platforms: Cases in India and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it