Can people be sentinels of sustainability? Identifying the linkages among ecosystem health and human well-being
Bibliographic record
Abstract
Human well-being depends on the health of ecosystems, but can human well-being also be an indicator of ecosystem health, and perhaps even sustainability? Research shows that ecosystem health and human well-being are often mutually reinforcing, whether in the direction of wellness and sustainability or poverty and degradation. However, while well-being is increasingly recognized as an important consideration when managing ecosystems, human needs and activities are often still thought of only in terms of their negative impacts on ecosystems. In this essay, we explore the proposition that there can be a mutually constitutive relationship between people’s well-being and the health of ecosystems, and discuss what such a relationship would mean for expanding the use of human well-being indicators in ecosystem-based management. Specifically, we discuss two areas of theory: ecosocial theory from social epidemiology and the marginalization–degradation thesis in political ecology; collectively, these provide a justification, in certain circumstances at least, for thinking of well-being as not just an add-on in natural resource management but as an indicator of ecosystem health and a prerequisite of social-ecological sustainability. We conclude with a discussion of future research needs to further explore how human well-being and ecosystem health interact.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".