Building momentum for a ‘policy turn’ in sustainability transitions: Lessons from Canada to consolidate strengths and bridge science-policy divides
Bibliographic record
Abstract
• The field of sustainability transitions is poised for a ‘policy turn’. • The field would do well to consolidate its strengths and address science-policy divides. • Strengths include defining the problem in terms of systems, driving at systems change, and proposing tuned governance solutions. • Divides relate to institutional embeddedness, connections between communities of research and practice, and an emphasis on theory versus actionable knowledge. Policymaking communities across a wide breadth of contexts are increasingly turning to the field of sustainability transitions to help inform the societal response to critical sustainability crises. Building on a legacy of science-policy affinity and after nearly a decade of rising policy engagement, the field is now poised to build momentum for a ‘policy turn’. However, to make more rapid progress in this regard, the field would do well to consolidate its strengths and address pressing science-policy divides. Based on practical experience engaging with policymakers and taking part in the climate policy process at the federal level in Canada, this policy brief offers reflections on what these strengths are and how to improve policy resonance going forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".