Sustainable development goal 14: To what degree have we achieved the 2020 targets for our oceans?
Bibliographic record
Abstract
Since the adoption of the United Nations Sustainable Development Goals in 2015, the world oceans, to which a specific goal was assigned, have been high on the global agenda. At the national level, the ocean has received increasing consideration, with many coastal states and islands adopting blue economy strategies and frameworks, and putting the ocean at the centre of development. SDG 14: Life Below Water includes ten targets, four of which (14.2, 14.4, 14.5 and 14.6) expired in 2020. This paper presents the state of progress on these four targets that address marine protection and fisheries management. The study is based on an assessment of the indicators established by the United Nations for each target, using publicly available databases allowing to measure the achievement of the targets. The analysis shows that achievement of these four targets is meagre. Only two countries achieved three of the four targets, while no country achieved all four. Most countries were classified as far from achievement or having made low progress. Across the four targets, SDG 14.5 on marine protected areas saw the highest number of achievers but also a high number of countries still far from achievement. Europe and Oceania had the highest number of countries having performed well in terms of achievement while Africa and the Middle East showed the most countries with limited achievement. These results indicate that there is still a long way to go to achieve these four targets in 2030. To move towards achievement, more investment is needed towards priority countries that have seen limited achievement but also some adaptation might be needed in terms of monitoring processes. Finally, it seems useful at this point to reflect on what has been achieved and how countries, especially those facing various socio-economic and political challenges, can fully benefit from current processes towards implementing SDG 14.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".