Creating inspiration: The role of the arts in creating empathy for ecological restoration
Bibliographic record
Abstract
Summary Australia’s natural areas have long been a source of inspiration for artists. Some of this art is a response to the natural environment or represents it in some way, and some is exhibited or performed in the natural environment itself. The objectives of this article are to: investigate the relationship between art and the artists who create it and people who make a living in extension, landcare or on the land; and, develop an understanding of whether the arts can encourage an emotional affinity with the natural environment. Eighty‐nine key informant interviews were conducted in Australia, the UK, USA, Canada and Norway, including people working in the arts and people working in natural resources management. Those in the latter group were people who either (i) worked in an extension (outreach) or community facilitation capacity, or (ii) people who played a voluntary role with community on‐ground restoration projects or who showed exemplary behaviour towards the environment. All the people working in natural resources management cited examples of visual or performing arts that they had found inspiring, that encapsulated or defined their attitudes towards the natural environment, or which reinforced their attitudes towards the natural environment. From these interviews, the idea of ‘chains of inspiration’ emerged. Each of the ‘chains’ began with people who worked directly on the environment. These people were in turn influenced or inspired by particular art from which they gained an emotional affinity towards the environment. The chains then progressed to the artists who created the art. All of the chains ended up with the natural environment from which the artists drew their inspiration. The paper concludes that by providing an emotional affinity or empathy for the natural environment, art can have a major role in influencing pro‐environmental attitudes.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".