A systematic scoping review of social sciences and humanities to contribute to ecosystem-based management
Bibliographic record
Abstract
Abstract Ecosystem-based management is key to achieving sustainable ocean use. To realize this potential, marine ecosystem-based management requires greater involvement of the social sciences and humanities, especially to adopt a more holistic approach and incorporate human–nature interactions. An understanding of the state of marine social science and humanities research and its potential to provide advice for management can inform and further its use. To contribute to a future where marine ecosystem-based management fully utilizes marine social science and humanities research, this analysis systematically scoped and reviewed 176 peer-reviewed social science and humanities papers about marine systems in Atlantic Canada published between 2000 and 2021. The analysis used ecological, economic, social/cultural, and governance objectives defined in an ecosystem-based management framework to structure the analysis. The analysis asked three questions: (i) What is the scope of the social science and humanities literature about aquatic systems in Atlantic Canada? (ii) How does that literature relate to objectives in ecosystem-based management? (iii) To what extent is that literature framed for practical integration of advice into decision making? Results indicate a comprehensive body of research, with potential to inform ecosystem-based management but with limited framing for practical integration. This result highlights missed opportunities for the research to be ready for use in ecosystem-based management. The research offers a framework, method, and strategies to understand and improve the scope and practical use of social science and humanities to inform marine ecosystem-based management in Atlantic Canada and globally.
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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 | 0.081 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.070 | 0.057 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".