National Accounting for the Ocean and Ocean Economy
Bibliographic record
Abstract
Abstract Realising the goal of the High Level Panel for a Sustainable Ocean Economy (Ocean Panel) to catalyse the transition to a sustainable ocean economy depends on coordinating and managing humanity’s relationship with the ocean and the broader environment. This task requires organising information that currently is often disorganised, spread across multiple government agencies or in a few cases not yet available. National ocean accounts would provide countries with the information needed to guide ambitious and broad- based plans to develop ocean economies and to capitalise on marine opportunities (European Union Directorate-General of Maritime Affairs and Fisheries and Joint Research Centre 2018; Economist Intelligence Unit 2015), while protecting the ocean for generations to come in accordance with the Sustainable Development Goals, most notably SDG 14, ‘Life below Water’. The ‘blue-ing’ of the ocean economy—or making the ocean economy sustainable—requires ensuring that the ocean continues to provide at least the current levels of opportunity; ‘measuring the ocean economy gives a country a first-order understanding of the economic importance of the seas’ (Economist Intelligence Unit 2015). The old adage goes that ‘what gets measured, gets managed’, or more accurately, that ‘if you cannot measure it, you cannot improve it’. Sound decision- making requires organised information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".