The Role of Public R&D Laboratories in Innovation Networks: a Comparison between Canada and Mexico
Bibliographic record
Abstract
The purpose of this study is to analyze those institutional arrangements that support the development of knowledge and innovation networks, particularly those that surround public R&D laboratories. Institutional arrangements include public policies as well as conditions that are necessary to build the proper kind of interactions among organizations. In Mexico, these processes are beginning to develop, in some cases with the support of regional-level policies. Internationally, different policy schemes have proven successful at the local and regional level. Policy development networks, where bottom-up and national and provincial directives interact, are being successfully implemented. In comparing the Mexican and Canadian experiences, some common characteristics can be identified that can lead to the development of adequate environments for innovation networks. Fieldwork reported here has consisted of organizational case studies of R&D laboratories in Mexico and Canada. These case studies include extensive documentation of organizational structure and practices, analysis of strategic planning documents and operating reports, as well as in-depth interviews with researchers, with higher management, and with representatives of organizations with which they interact. Interviews also include government agency officials and representatives of firms that have used or sought to use the laboratories' services. Networks studied include fuel cells technology, medical biotechnology, agro biotechnology, electro chemistry, metal mechanics and software industry, for example. The comparison of several innovation networks in Canada, which are more developed, with those that are beginning to develop in Mexico, enables the identification of viable alternatives for the design and implementation of policy initiatives for their development. Implications for further research and for policy design and implementation are discussed, particularly with respect to the role that R&D labs can play in policy implementation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".