Ecotone: Finding Common Ground Across Art, Science and Ranching
Bibliographic record
Abstract
This paper uses the case study of Ecotone, a project that sought to bring disparate groups of people (artists, scientists, ranchers) together for shared discourse and potential action around agricultural environmental stress in southern Alberta, Canada. We explore this project from the perspective of an artist and designer. We examine a framework that values space, time and the pairing of people from different disciplines to encourage meaningful collaboration and interaction. Environmentalism and climate change are divisive topics, particularly in Alberta where the controversial oil and gas industry has made it Canada’s wealthiest province, resulting in both environmental indifference as well as extensive protests locally and from abroad. It is well acknowledged there is a need for better communication about the environment for real progress in protecting our resources to begin. Ecotone begins this conversation by inviting artists and designers to respond to the science and pragmatic realities of land stewardship.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.036 | 0.060 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".