Sustainable development and subnational governments: Policy-making and multi-level interactions
Bibliographic record
Abstract
Almost 25 years have passed since the Brundtland Report drew world-wide attention to the concept of sustainable development. While global summits such as the ones in Rio and Johannesburg gave important impulses in the search for sustainable development policies, concrete efforts for the implementation of the concept have been made at different levels of governance. At the eve of a new ‘Rio +20’ summit, the debate on the implementation of Agenda 21 and the policy principles of the Rio Declaration is still ongoing. This paper emphasizes the importance of the subnational level of governance and discusses how subnational governments have taken up the challenge to institutionalize sustainable development and design sustainable development policies. It presents the findings of a transatlantic research effort that focused on two research questions. First, how have subnational governments taken up the challenge to institutionalize sustainable development and design sustainable development policies? Second, how do subnational governments try to be involved in international decision-making for sustainable development and how do they interact with other levels of governance? The paper brings together empirical material of experiences of subnational governments from Belgium, Canada, Germany, the Netherlands, Spain, the UK and the US, gathered through interviews, non-participatory observations and document analyses.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".