Artists’ and Creators’ Reframed Relationship with Nature Since the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract This report is part of a wider research project, Reframing Creativity, which studied how the COVID-19 pandemic affected the work and creative practice of professional artists, producers and makers. Here we discuss a specific finding about artists’ and creators’ relationships with nature. After conducting a first round of interviews with 11 participants, we identified that around half of them had talked about having found a valuable connection with nature since the pandemic—even though nature was not a topic in our sequence of questions. This led to a deeper analysis of nature and creativity through a second round of interviews with 11 further participants. For both rounds of interviews, we used a semi-structured questionnaire with a snowball sampling method for recruitment. We conclude that artists and creators developed new meanings and perspectives on their relationship with the outdoors as an unexpected result of the new first-hand experiences they were able to have outside, that is, as a result of the opportunities the pandemic enabled. We also argue that creators face an urgent need to find a healthy balance between the unstoppable advancement of digital technologies, accelerated by the pandemic, and the fundamental need to be connected with the natural world. These new creator-nature connections should be fostered, preserved, and researched further.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".