Reflective Appraisal of Transformative Innovation Policy: Development of the Sustainability Transition and Innovation Review (STIR) Approach and Application to Canada
Bibliographic record
Abstract
In the context of governing innovation systems for low-carbon transitions, learning is paramount. In this article, we look specifically at the issue of learning through innovation policy review processes. We begin by reviewing the academic literature on innovation policy reviews as well as the emerging literature on transformative innovation policy (TIP) in the contexts of major challenges such as climate change. Drawing from this review, we argue that traditional policy review frameworks fail to provide the kind of learning required to assess challenge-oriented innovation policies and that new, more reflexive approaches are required. We then propose a novel evaluative framework, the Sustainability Transition and Innovation Review (STIR), which incorporates insights from the TIP literature in order to address this gap. The basis for the proposed STIR framework in the theoretical literature is reviewed, and we then describe the results of a test of the STIR framework to evaluate Canada’s policy mix for driving a socio-technical transition from fossil-powered to electric vehicles. Insights from the test application show that the STIR approach helped uncover key explanatory dynamics around incremental vs. transformative change in Canada’s innovation policy performance, and highlighted the interplay between governance and substantive weaknesses in the policy mix. We conclude by arguing that these findings demonstrate the importance of updating policy review frameworks with the insights of the recent TIP literature.
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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 | 0.164 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".