A practical approach for putting people in ecosystem‐based ocean planning
Bibliographic record
Abstract
Marine and coastal ecosystems provide important benefits and services to coastal communities across the globe, but assessing the diversity of social relationships with oceans can prove difficult for conservation scientists and practitioners. This presents barriers to incorporating social dimensions of marine ecosystems into ecosystem‐based planning processes, which can in turn affect the success of planning and management initiatives. Following a global assessment of social research and related planning practices in ocean environments, we present a step‐by‐step approach for natural resource planning practitioners to more systematically incorporate social data into ecosystem‐based ocean planning. Our approach includes three sequential steps: (1) develop a typology of ocean‐specific human uses that occur within the planning region of interest; (2) characterize the complexity of these uses, including the spatiotemporal variability, intensity, and diversity thereof, as well as associated conflicts and compatibility; and (3) integrate social and ecological information to assess trade‐offs necessary for successful implementation of ecosystem‐based ocean planning. We conclude by showing how systematic engagement of social data – together with ecological information – can create advantages for practitioners to improve planning and management outcomes.
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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 | 0.041 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 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".