Extreme environments: An educational framework for arts-based field research
Bibliographic record
Abstract
Field station research locations offer scientists isolation and immersion for more precise statistical analysis of climate change and environmental damage. As more art/science initiatives develop in academia, art students are gaining access to difficult scientific research sites and using the experience to fuel creative strategies. The methodology for offering a course that taps these into possibilities for the teaching of creativity remains little explored. Through a case study at the School of Creative Media in Hong Kong, this article examines how student expeditions that work adjacent to environmental scientists in extreme environments can be used for the teaching of creativity and artistic process as well as informing a larger public on climate issues. The structure of the program with detailed descriptions of sequenced proficiencies is presented. Both pedagogical philosophy and logistic issues will be discussed through the set-up and organizational structure of the course, the variety of teaching materials, assignments, dissemination and finally the exhibition and impact of the students’ work. Using scientific resources with the goal of artistic interpretation, the pedagogy is designed to respond to the emerging potential of digital technologies in creative media. The results, both for the students and the public, demonstrate multimodal approaches that offer broader possibilities for learning and outreach that are both scalable and transferable.
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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 | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".