The living infinite: Envisioning futures for transformed human-nature relationships on the high seas
Bibliographic record
Abstract
We find ourselves at a critical crossroads for the future governance of the high seas, but the perceived remoteness of the global ocean creates a psychological barrier for people to engage with it. Given challenges of overexploitation, inequitable access and other sustainability and equity concerns, current ocean governance mechanisms are not fit-for-purpose. This decade offers opportunities for direct impact on ocean governance, however, triggering a global transformation on how we use and protect the half of our planet requires a concerted effort that is guided by shared values and principles across regions and sectors. The aim of the series of workshops outlined in this paper, was to undertake a futures thinking process that could use the Nature Futures Framework as a mechanism to bring more transformative energy into how humans conceptualise the high seas and therefore how we aim to govern the ocean. We found that engaging with the future through science fiction narratives allowed a more radical appreciation of what could be and infusing science with artistic elements can inspire audiences beyond academia. Thus, creative endeavours of co-production that promote and encourage imagination to address current challenges should be considered as important tools in the science-policy interface, also as a way to elicit empathetic responses. This workshop series was a first, and hopefully promising, step towards generating a more creative praxis in how we imagine and then act for a better future for the high seas.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".