Avoiding a crisis of motivation for ocean management under global environmental change
Bibliographic record
Abstract
Climate change and ocean acidification are altering marine ecosystems and, from a human perspective, creating both winners and losers. Human responses to these changes are complex, but may result in reduced government investments in regulation, resource management, monitoring and enforcement. Moreover, a lack of peoples' experience of climate change may drive some towards attributing the symptoms of climate change to more familiar causes such as management failure. Taken together, we anticipate that management could become weaker and less effective as climate change continues. Using diverse case studies, including the decline of coral reefs, coastal defences from flooding, shifting fish stocks and the emergence of new shipping opportunities in the Arctic, we argue that human interests are better served by increased investments in resource management. But greater government investment in management does not simply mean more of "business-as-usual." Management needs to become more flexible, better at anticipating and responding to surprise, and able to facilitate change where it is desirable. A range of technological, economic, communication and governance solutions exists to help transform management. While not all have been tested, judicious application of the most appropriate solutions should help humanity adapt to novel circumstances and seek opportunity where possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".