Digital elixir? The Aakash tablet as a social innovation in information and communication technology for development (ICT4D)
Bibliographic record
Abstract
Purpose The Aakash tablet was developed as a social innovation (SI) to transform India’s higher education sector. This paper aims to explain the failure of the Aakash tablet beyond the typical explanations of deficiencies in the device’s technical capabilities. This paper argues that an SI lens provides a stronger explanation for its failure than the standard analyses built primarily on the technological viability of the device. Design/methodology/approach The Aakash project ran from 2010 to 2015. During this period, a number of government and policy reports as well as mainstream media articles were published on the device. Since 2015, a number of academic articles have been published on the Aakash emphasizing its failure as a technological solution. The authors draw on these sources to frame an understanding of the Aakash’s failure informed by SI theory. Findings Through a complexity-informed analysis, the authors show that the failure of the Aakash stemmed from flawed assumptions and a failure of the initiative to engage with both the particular and constantly changing features of the broader landscape of needs and opportunities. Originality/value This study draws attention to failure as a legitimate aspect of the study of SI. In presenting a “counter-case” to the usual success stories, it shows that the SI lens can also explain why an SI does not take off. It thereby adds to the literature on SI and complexity theory through an exploration of the complex interactions among public policy goals, technological advancements and entrepreneurship.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".