Evaluating indicators of human well-being for ecosystem-based management
Bibliographic record
Abstract
ABSTRACT Introduction: Interrelated social and ecological challenges demand an understanding of how environmental change and management decisions affect human well-being. This paper outlines a framework for measuring human well-being for ecosystem-based management (EBM). We present a prototype that can be adapted and developed for various scales and contexts. Scientists and managers use indicators to assess status and trends in integrated ecosystem assessments (IEAs). To improve the social science rigor and success of EBM, we developed a systematic and transparent approach for evaluating indicators of human well-being for an IEA. Methods: Our process is based on a comprehensive conceptualization of human well-being, a scalable analysis of management priorities, and a set of indicator screening criteria tailored to the needs of EBM. We tested our approach by evaluating more than 2000 existing social indicators related to ocean and coastal management of the US West Coast. We focused on two foundational attributes of human well-being: resource access and self-determination. Outcomes and Discussion: Our results suggest that existing indicators and data are limited in their ability to reflect linkages between environmental change and human well-being, and extremely limited in their ability to assess social equity and justice. We reveal a critical need for new social indicators tailored to answer environmental questions and new data that are disaggregated by social variables to measure equity. In both, we stress the importance of collaborating with the people whose well-being is to be assessed. Conclusion: Our framework is designed to encourage governments and communities to carefully assess the complex tradeoffs inherent in environmental decision-making.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".