From the Bubble to the Core. Long-Term Competitive Advantages for Emerging Markets through Innovation in the Extractive Industry
Bibliographic record
Abstract
As dependence on the extraction of natural resources for three BRICS countries studied in this paper (Brazil, Russia and South Africa) seems inevitable in the short and even medium-term perspective, these countries will face the need for deep modernization of their extractive industries. This paper sets out to analyze the R&D policies of Brazil, Russia, and South Africa; including Canada with its sizeable and innovative extractive industry to offer a perspective for benchmarking. The methodology of the research combines content analysis of major scientific publications and monitoring research results, as well as policy analysis of key national government regulations in place. We consulted the data produced by major international statistical agencies like the OECD Statistics Directorate and Eurostat. Besides that, there are not many differences in the innovation policy instruments used in developed countries vs. fast growing economies. Rather, it is their synergy, governance, targeted design, and application that make up all the differences. All four countries that were studied emphasized the overarching R&D-related policy goals like achieving a certain GDP percentage of R&D investment. However, it seems that definite fine-tuning of policy tools and structural reforms successfully implemented in developed countries is required in the case of developing countries. Future research should focus more on the necessity of a fine-tuned policy mix for commodity-based economies to the requirements of the existing industry base. As the entrepreneurial activity in these countries is naturally limited and clustered around resource-based industries, research on policy-making should more strongly focus on companies of this sector and their influence on entrepreneurship for the economy as a whole.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".