Convergence and Divergence in ‘New Governance’ Arrangements: Evidence from European Integrated Natural Resource Strategies
Bibliographic record
Abstract
To analyse convergence and divergence in Natural Resource New Governance Arrangements (NRNGAs) two regimes in the environmentally-related areas of forest and fisheries management are examined. The findings reveal limited convergence across sectors and countries in the general aims and ideas behind NGAs and evidence of significant policy divergence in the tools and mechanisms created for their implementation. The reasons for the differences lie primarily in the policy formulation process. While the impetus for the adoption of both NRNGAs is in the international and regional realms, without the force of either international law or competitive advantage, pressure for convergence is weak. Aspects of the policy formulation process, especially the manner in which the changing capacities of domestic public and private actors active in the affected resource policy arena interact to influence policy design, are critical for explaining policy convergence and divergence. Specifically, the interplay between the effect of the internationalization of resource policy issues, tending to increase private capacities at the expense of the public one, and the declining importance of primary industries, which has the reverse effect, is shown to have played an important role in NRNGA policy dynamics.
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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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".