Reverse Innovation and the Bottom of the Pyramid Proposition
Bibliographic record
Abstract
Analysing cases from India, this chapter reveals flaws in recent claims that reverse innovation can resolve some of the world's most urgent social problems. Reverse innovation implies the diffusion of innovations from developing to developed countries, and is therefore, per se, irrelevant for the social needs of the former. If understood more broadly, as a strategic approach, reverse innovation may reduce some dimensions of inequality. However, as an instrument of poverty reduction, reverse innovation equals the known and compelling but doubtful proposition that developed country multinational enterprises may induce large-scale prosperity simply by doing business with the world's poorest. In this chapter, the authors assess the social impact of reverse innovations and contrast previous wholesale claims on those impacts with an in-depth analysis. The authors’ analysis reveals that these social impacts are not as significant as currently believed. The chapter concludes by suggesting future research avenues on the bottom of the pyramid, which will be of key relevance to academics and managers alike.
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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 | 0.000 |
| 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 | 0.002 |
| 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".