Marinising a terrestrial concept: Public money for public goods
Bibliographic record
Abstract
Exiting the EU allows the UK to unilaterally change the frameworks that govern its environment and natural resources . This opportunity is timely given the urgent need to address the biodiversity and climate emergencies, and deliver the necessary policy changes to meet associated international agreements. The UK's divergence from EU environmental policy has already begun. The new Agriculture Act uses the concept of “public money for public goods” (PMPG) to seemingly revolutionise direct agricultural subsidies, replacing the much-maligned funding mechanisms under the Common Agricultural Policy and making the provision of their replacement dependent upon actions delivering societal gain. However, the potential benefits of transposing this concept to marine fisheries and aquaculture are yet to be recognised despite similar criticisms of funding mechanisms under the Common Fisheries Policy . This paper therefore considers the key distinctions between our use of marine and terrestrial environments and how PMPG could be applied to fisheries and aquaculture. The findings suggest that some forms of aquaculture are well-placed to benefit from a ‘marinising’ of the PMPG concept. Currently, capture fisheries, because they do not have ownership over marine space and interact with the marine environment in an extractive manner, have a greater challenge to adapt their business models to receive public money under this framework.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 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".