Bibliographic record
Abstract
The notion of national innovation systems has come into prominence as a way of illuminating the processes, institutions and actors responsible for industrial innovation and economic growth at the country level. There is a tendency among policy makers and analysts, however, to regard the notion as an overly precise tool with which to achieve national prosperity rather than an attempt to more fully understand the institutional and human dynamics which give rise to innovation. We employ second-order cybernetics to counter this tendency. Using new information, some contained in taxation statistics and some contained in an industrial demographics database under development, we demonstrate that innovation is much more widespread and involves many more people than commonly thought. Contemporary pictures of sparsely occurring innovations are shown to be misleading-at least in the case of Canada. This finding invites a rethinking of innovation policies. A new epistemology is needed to account for the ubiquity of the innovation phenomenon and to include actors and factors not usually ascribed to national innovation systems. We suggest, for example, that technologists and technicians often figure prominently in the creation of specific innovations but seldom get any credit for their role. We also point to other, less tangible, factors, such as trust, which surely enter the picture, but are seldom counted as influences that can encourage or stifle industrial innovation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".