Identifying Ocean‐Related Literature Using the UN Second World Ocean Assessment Report
Bibliographic record
Abstract
In recent years, ocean governance has called for strategic action and science‐informed policy to work towards the sustainable development of the ocean, most notably as part of the UN Decade of Ocean Science for Sustainable Development (2021–2030). This common framework identifies the integration of scientific knowledge in governance as a key process to deliver solutions responding to the current challenges, opportunities, and transformations posed by global change in the oceans. This article presents a methodological approach for identifying ocean‐related research outputs and documenting research‐based knowledge integration in documents that inform ocean governance. Specifically, this study builds on an analysis of the references included in the UN Second World Ocean Assessment report to (a) identify and describe the research outputs cited in the distinct chapters of the report, (b) identify research outputs relevant to ocean governance through the analysis of citations from and to references included in the UN Second World Ocean Assessment, (c) compare both datasets to examine the position of the literature cited in the report within a broader ecosystem of ocean‐related research, and (d) present a method to identify topically relevant research that could be integrated in future ocean assessments. Our findings show distinct referencing practices across chapters and expert groups and a higher reliance on high‐profile sources in the report compared to a broader dataset of ocean research outputs. Moreover, this study highlights an innovative approach to identifying ocean research based on knowledge syntheses and considers discussion points about integrating research‐based knowledge in documents informing ocean governance.
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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 | 0.028 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.170 | 0.181 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".