Commissioning community-based art projects to support engagement with nature-based solutions
Bibliographic record
Abstract
Understanding social-ecological connection is paramount to adoption and long-term viability of nature-based solutions (NbS). Here, we describe a three-year trial of community-engaged participatory research (CBPR) through an artist commission program run by Engage with Nature-based Solutions ( http://www.engagewithnbs.ca ). The program has thus far commissioned twelve Canadian artists to contribute their artistic research to facilitate conversations about climate change and conceptualizations of NbS. The artists we commissioned created a piece of art for their local community on NbS and climate, facilitated a community-engaged workshop to share their research creation, and supported the development of an online toolkit meant to help other communities engage with NbS and climate change. We suggest that this commission program is a cost-effective way to: (i) reach a diversity of communities typically outside the reach of academia, (ii) enlarge audiences who are engaged with NbS, (iii) provide alternative formats and mediums for engagement and education on NbS, (iv) give credence to artistic work as climate work, and (v) provide opportunities for collaboration between the arts/sciences and community/academia.
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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 | 0.049 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".