To Achieve a Sustainable Blue Future, Progress Assessments Must Include Interdependencies between the Sustainable Development Goals
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs) were designed to address interactions between the economy, society, and the biosphere. However, indicators used for assessing progress toward the goals do not account for these interactions. To understand the potential implications of this compartmentalized assessment framework, we explore progress evaluations toward SDG 14 (Life below Water) and intersecting social goals presented in submissions to the UN High-Level Political Forum. We show that there is a disconnect between the apparent progress shown by indicators and long-term sustainability; for example, short-term gains in reducing hunger or poverty might be undermined by poor ocean health, particularly in countries dependent on fisheries or developing their blue economy. We suggest an extension to existing indicator assessments to integrate scenarios and social-ecological modeling. This approach would ensure that decision makers are provided with knowledge fundamental to directing actions to attain SDGs while minimizing unintended outcomes due to interactions among goals.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".