Government-affiliated intermediaries in climate policy: managing “productive tensions” between flexibility and control
Bibliographic record
Abstract
Abstract While there is growing agreement that government-affiliated intermediaries can be an asset in advancing climate policy, perspectives diverge on the precise governance arrangements and conditions under which they excel. Some view government-affiliated intermediaries as instruments that states can exert control over to achieve their centrally determined objectives. Others contend that such bodies work best when they have the autonomy to experiment and shape policy formulation. This article seeks to clarify these debates by demonstrating that there is value in adopting governance arrangements that keep these two approaches (instrumental/experimental) in tension. The argument here is that a governance approach, which balances instrumental and experimental logics, can generate “productive tensions” to manage trade-offs between flexibility and control. Using a process-tracing analysis, the article explores this argument through a case study of a government-affiliated intermediary in Quebec—Propulsion Québec—deliberately created by the state to intermediate between the public and private sectors in the electric transportation sector. Findings reveal productive tensions between the state and a government-affiliated intermediary, as well as between different government-affiliated intermediaries, but show that these tensions can be difficult to sustain over time. Overall, attending to these tensions allows for a deeper understanding of the governance arrangements and conditions under which government-affiliated intermediaries can advance climate policy.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".