Institutions for Technology Diffusion
Bibliographic record
Abstract
This technical note analyzes international experiences and practices of public technology extension service programs. Technology extension services comprise varied forms of assistance provided directly to enterprises to foster technological modernization and improvement, with a focus on established small and mid-sized enterprises. The note discusses the definitions, rationales, and characteristics of selected technology extension service programs, drawing on examples from Europe, North America, and other regions. It presents four detailed case studies: the U.S. Manufacturing Extension Partnership; the National Research Council-Industrial Research Assistance Program in Canada; England's Manufacturing Advisory Service; and Tecnalia, an applied technology organization in Spain. The case studies address several program elements including the history and evolution of the program, structure, program scale, financing structure, services and clients, governance, personnel, monitoring, and evaluation. The analysis highlights common and distinctive characteristics as well as program strengths, weaknesses, and key practices. The note provides a framework for positioning technology extension services within the broader mix of policies for technology transfer, business upgrading, and innovation, and offers conclusions and insights to support efforts to strengthen technology extension services in Latin America.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".