Arts Engagement with Sustainable Communities: Informing New Governance Styles for Sustainable Futures
Bibliographic record
Abstract
In established processes of governance and related literature, arts and culture have been largely neglected, but work is currently being produced suggesting the importance of arts and culture to processes of good governance and the sustainability transition. A style of governance that fully integrates cultural considerations and understands cultural implications of policy is desirable to address the integrated aims of sustainability and to guide the transition to a sustainable future. The impact of arts and culture on communities and social perceptions is difficult to assess and to anticipate; similarly, the influence of culture on governance and policy is equally difficult to measure, though culture permeates every aspect of social and political life. This article suggests that taking cues from the processes of arts and culture to inform new styles of governance supports an open, adaptive, participatory, and creative governance model that responds to a diversity of voices and alternative modes of communication. It argues that a governance style that integrates cultural knowledge is better able to build equity across present and future generations, and is better suited to support a sustainable future. Empirical examples of arts engagement with two communities practicing sustainable behaviours demonstrate the power of arts and culture to build social capital and to potentially contribute to an inclusive and innovative style of governance.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".