“Can the participation of civil society in policy networks mitigate against societal challenges in rural areas?”
Bibliographic record
Abstract
Rural areas increasingly face a raft of societal economic, social and place-based challenges, which civil society potentially has a role to play in mitigating. However, there are gaps in extant knowledge as to whether policy networks are present in rural areas and if they are, what, if any, role civil society plays in these in seeking to mitigate societal challenges in rural areas. Initially, we show how the societal challenges of rural areas may be addressed through policy networks. Policy network theory guides our analysis in eight rural areas in Denmark. The analysis is based on semi-structured interviews with 38 local stakeholders from in- and outside civil society. Based on the literature reviewed, we establish four dimensions that are important for the success of policy networks in mitigating societal challenges. In our analysis, we focus on these four dimensions in assessing the presence and role of policy networks in rural areas. The four dimensions describe the extent to which 1) collaboration occurs amongst a wide selection of representatives from civil society, other local stakeholders and local government, 2) steering from local government is characterized by ‘strategic signposting’ and trust, 3) local stakeholders are invited early into decision-making processes and influence them and 4) a mutual resource dependency is observed. We conclude by discussing to what extent the type of policy network found is able to mitigate the pressing societal challenges of rural areas and, finally, we make recommendations for how to support civil society in contributing to the mitigation of these societal challenges at three levels (local government, associations and citizens). • Rural areas increasingly face a raft of economic, social and place-based challenges for which civil society potentially has a role to play in mitigating. • The societal challenges of rural areas might be mitigated through governance policy networks. • We find that policy networks are present in rural areas, and they have a role in mitigating societal challenges in rural areas. • We make recommendations for how to support civil society in mitigating societal challenges in rural areas on three levels (local government, associations and citizens).
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".