Doubling Down on Wicked Problems: Ocean ArtScience Collaborations for a Sustainable Future
Bibliographic record
Abstract
The UN Decade of Ocean Science for Sustainable Development recognizes the current ocean sustainability crisis and calls for a transformation of ocean science. Many of the key challenges recognized by the UN Decade are examples of wicked problems: intractable and messy situations with high stakeholder divergence. Addressing wicked ocean sustainability problems requires adaptable, iterative, and participatory approaches that can embrace multiple ways of knowing. It also requires a re-imagining of our relationship with the Ocean from extraction and resulting environmental degradation, towards the building of a sense of connection and stewardship. We propose ArtScience as a means to this end by highlighting how transdisciplinary collaborations can help create sustainable ocean futures. We reflect on a recent ArtScience event emerging from Ocean Networks Canada’s Artist-in-Residence programme. By situating ArtScience in a broader context of inter- and transdisciplinary collaborations, we demonstrate how ArtScience collaborations can help transform ocean science by envisioning previously unimagined possibilities, and establishing and strengthening relationships with diverse stakeholders through long-term mission-driven or place-based inquiry. We conclude with a call to action to acknowledge the potential these collaborations hold for addressing the challenges of the UN Ocean Decade.
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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 | 0.030 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.046 | 0.037 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.003 | 0.045 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 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".