Innovation and Entrepreneurship in Emerging Economies
Bibliographic record
Abstract
It is nowadays taken for granted that innovation and entrepreneurship are indispensable for economic development. Yet, research on these topics has been mostly done in the context of developed nations. This suggests an important blind spot in the economics and management literatures. Literature is incomplete because it falls short of an adequate understanding of entrepreneurship and innovation in the development process. More than a billion people still live in extreme poverty. How does entrepreneurship and innovation matter to these bottom billion and what does this imply for our understanding of the role of the entrepreneurial or the innovation processes is thus of critical relevance. This symposium addresses a set of complementary topics investigating the role of entrepreneurship and innovation processes in a variety of emerging economies, from China to Bangladesh, the Philippines or Africa.Network Ties or Institutional Rules: How Do Entrepreneurs Innovate in Emerging Economies? Presenter: Daniel Armanios; Stanford U.Presenter: Chuck Eesley; Stanford U.Presenter: Jizhen Li; Tsinghua U.Innovation by users in emerging economies: Evidence from mobile banking services Presenter: Paul van der Boor; Catolica Lisbon / Carnegie MellonPresenter: Pedro Oliveira; Catholic U. of Portugal - FCEEThe Role Of Entrepreneurship In The African Mobile Industry GrowthPresenter: Mohammad Jahanbakht; Carnegie Mellon U.Presenter: Rui Baptista; Instituto Superior TecnicoPresenter: David Hounshell; Carnegie Mellon U.Presenter: Francisco Veloso; Catolica Lisbon / Carnegie MellonFactor Market Imperfections and Pre-Entry Experience: Spinoffs in the Bangladesh Garment IndustryPresenter: Romel Mostafa; U. of Western Ontario
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".