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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".