How to enhance continuity of strategic sustainable development work in municipalities and regions – findings from Sweden and Finland
Bibliographic record
Abstract
Despite numerous initiatives, methodologies and approaches, municipalities and regions continue to face challenges in sustaining long-term efforts toward sustainability. While continuously learning from mistakes and success factors, comprehensive recommendations for how to take a long-term approach for the work, specifically developed for leadership in local and regional contexts, are largely lacking. Therefore, the purpose of this study was to identify a comprehensive set of essential factors that could support long-term sustainability efforts. The study is mainly related to the Framework for Strategic Sustainable Development, but the findings are likely relevant also in relation to other comprehensive sustainability methodologies and approaches. Re-analysis of longitudinal data from action research in Sweden and Finland is supplemented by new qualitative data collection, the study identifies eight conditions for continuity. Being introduced to these conditions upfront may save valuable time and could be utilized as a checklist to be revisited regularly and to strive toward.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".