Bibliographic record
Abstract
In the face of the climate emergency, it is becoming clear that cultural change is a necessary transformative shift that must occur to ensure human survival. Climate change is entangled with behavioural and social dimensions of our lives, necessitating that we undergo cultural transformation to access the potential of existing climate solutions. While there is both an increase in research regarding how the arts can contribute to this needed cultural transformation, as well as increasing participation in climate work by those within the arts sector, the marriage between evidence and practice in this field is in its infancy. Existing literature highlights the exciting potential of the arts to make meaningful contributions to climate action through interdisciplinary contributions to knowledge creation, public engagement forums that go beyond fact-sharing, and imagining future scenarios for our world. That said, arts organizations are often left out of the conversation. In an effort to bridge the gap between study and practice in this field, this paper reports on interviews with key members of CreativePEI to better understand how one arts organization and its members conceptualize their role in climate action as well as identifying critical barriers to conducting climate work within the arts. Further, the paper situates the results of the study within the current literature, examining any synergies between the findings of the study and scholarly works in the field. By showcasing the ways in which one arts organization situates itself within the broader project of climate change, this work sheds new light on the current state of climate work in the arts in Canada and how cultural organizations can reimagine their role to better align with the evidence about what the arts can uniquely offer to climate action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".