Building knowledge regions in developing nations with emerging innovation infrastructure: evidence from Mexico and Pakistan
Bibliographic record
Abstract
This paper studies the efforts of building knowledge regions in emerging economy nations with special reference to Mexico and Pakistan. It starts with the introduction of an analytical framework developed for assessing knowledge regions. This is followed by case studies exploring the emerging innovation infrastructure appearing in several metropolitan regions of the two countries (five in Mexico and three in Pakistan) aimed at developing knowledge-based economies. A comparative analysis of the existing structures and policies of each case shows that efforts have been made primarily through university and research centre initiatives, while support programs such as science parks, incubators and other government incentives seem to have only limited effectiveness except when used in regions with a well developed industrial and service base and related entrepreneurial culture. Overall, there are gaps in innovation infrastructure development due to scarce resources, as well as in most cases, absence of entrepreneurial culture, both considered longer term undertakings. While providing insights into the challenges faced by developing nations when building knowledge regions, the paper lays out what can be learned from both countries' experiences and recommends appropriate policy actions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".