Expanding the scope and roles of social sciences and humanities to support integrated ecosystem assessments and ecosystem-based management
Bibliographic record
Abstract
Abstract Understanding social-ecological systems (SESs) is an important part of ecosystem-based management (EBM). One of the main decision support frameworks to develop scientific advice for EBM is integrated ecosystem assessments (IEAs). Human dimensions in SESs are primarily captured through indicators derived from three social sciences: economics, anthropology, and sociology. The breadth of social sciences and humanities (SSH) research is much greater than those three fields, but they are generally underused in natural science-based decision support processes such as IEAs. Greater contributions of SSHs can enhance IEAs through various direct (e.g. to develop indicators) and indirect ways (e.g. to establish and maintain ethical practices). We examine a wider range of SSH disciplines and conclude that scientific advice processes that inform EBM can benefit from broader integration of SSH theories and methods through themes of contextualizing, facilitating, communicating, evaluating, and anticipating. We see this an opportunity to both widen the vocabulary used to describe social scientists and those who work in humanities in IEAs, and apply the underlying worldviews used to conduct SSH research to fundamentally enhance the IEA process and to further progress in EBM.
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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 | 0.112 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".