Innovation for all? Legitimizing science, technology and innovation policy in unequal societies
Bibliographic record
Abstract
Most of the policy lessons concerning Science, Technology, Innovation (STI) policy promoted by international organizations derive from the experience of industrialized countries. The realities in later industrializing countries are different: inequality rates remain high, while poverty and redistribution dominate their political agendas. As a means to socio-economic development STI policy has increasingly gained importance in public policy in the developing world. This thesis seeks to understand how governments in later industrializing countries with high rates of inequality (LICHIR) legitimize and develop their STI policies. The author argues that governments in LICHIR face multiple and competing policy burdens. To overcome these burdens, political actors try to legitimize and to develop their STI policy responding to both domestic and international driving forces. These policy forces are fuelled by three sources of legitimacy: internationalization, technonationalism and social development. Evidence derives from a comparative analysis of the STI policy processes in South Africa and Brazil from 1990- 2010. Quantitative and qualitative data come from the Global Innovation Index, policy and research documents and a sample of 99 interviews with actors in government, industry and academia.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.053 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".