Local-Global Partnerships for High-Tech Development: Integrating Top-Down and Bottom-Up Models
Bibliographic record
Abstract
Advocates of high-tech development use conflicting top-down and bottom-up models to respond to the challenge of the increasing knowledge intensity of the global economy. The trend for policies in the United States, Canada, and Australia is to shift the emphasis from federal government and external resources to increased state and local responsibility. The competing top-down and bottom-up approaches are reviewed and then illustrated with case studies. Canada’s Technology Triangle and Australia’s Multi-Function Polis were both initiated in 1987 and then transformed in 1997. The evaluation of these case studies identifies weaknesses in the original models and calls for the integration of the two development approaches into a model of local-global partnership for high-tech development based on the building of local capacity through partnerships with local and external actors.
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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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".