Governing complex environmental policy mixes through institutional bricolage: lessons from the water-forestry-energy-climate nexus
Bibliographic record
Abstract
Policy mixes come in many shapes and sizes. This poses many challenges to policy design, especially when mixes extend across sectors and have multiple levels. This is the case with the Water-Forest-Energy-Climate (WFEC) nexus, a complex policy mix that involves not only significant cross-sectoral linkages and the potential complementarities and conflicts which are examined in other articles in this special issue, but also deals with sectors which involve significant national and trans-national elements. This complex multi-sector, multi-level policy assemblage also lacks the cohesion provided by a treaty-based international regime which allows multi-level co-ordination and integration of policy designs in areas such as trade or finance. In such policy non-regime or weak regime complexes, regional agreements and the negotiated nature of interactions within such agreements (which we see as a form of ‘policy bricolage’) are critical but overlooked factors affecting policy success.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".