Technology platforms as an ICT4D model for business development
Bibliographic record
Abstract
Platform technologies provide cost-effective models of exchange, use a huge amount of data to suggest different products for customers and create a scalable space for the unprivileged and underserved entities to do business online. Yet the unlimited opportunities created by platform technologies can collapse into different forms of institutional voids and socio-economic inequalities. This editorial discusses the unique benefits of platform technology and ICTs for business development and explore emergent knowledge of consumers as an opportunity to shape the design, implementation, use and evaluation of platform-based business models that can address institutional voids and be trusted for development. Taking inspiration from emergent knowledge and articles published on the potential of platform technology and ICT infrastructures for development in this Special Section, we advance Ciborra’s original conceptualization of platform as a unique organizing technology to innovate organizations by arguing that platform-based business models can be positively leveraged to mitigate institutional voids in marginalized communities. This offers an insightful contribution for researchers, practitioners and policymakers to empower consumer participation in the design of platform-based business models to maximize the full and equitable potential of technology platforms and ICTs for business and socio-economic development.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".