Science-Industry Collaboration: Sideways or Highways to Ocean Sustainability?
Bibliographic record
Abstract
There is substantial and unexplored potential for scientists to engage with the private sector for a sustainable ocean. The importance of such cooperation is a frequent emphasis of international dialogues and statements, it is embedded within the Sustainable Development Goals, and has been championed by prominent business leaders and scientists. But an uncritical embrace of science-industry collaboration is unhelpful, and candid reflections on the benefits and pitfalls that marine scientists can expect from actively engaging with the private sector are rare. In this Perspective, we draw on our collective experiences working with ocean industries in different parts of the world to reflect on how this has influenced our work, the effects these collaborations have generated, and the barriers to overcome for such partnerships to become more common. In doing so, we hope to help empower a new generation of marine scientists to explore collaboration with industry as a way to develop and scale up solutions for ocean sustainability.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.029 | 0.052 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".