Ecosystem Services and Beyond: Using Multiple Metaphors to Understand Human–Environment Relationships
Bibliographic record
Abstract
Ecosystem services research has been focused on the ways that humans directly benefit from goods and services, and economic valuation techniques have been used to measure those benefits. We argue that, although it is appropriate in some cases, this focus on direct use and economic quantification is often limiting and can detract from environmental research and effective management, in part by crowding out other understandings of human–environment relationships. Instead, we make the case that the systematic consideration of multiple metaphors of such relationships in assessing social–ecological systems will foster better understanding of the many ways in which humans relate to, care for, and value ecosystems. Where it is possible, we encourage a deliberative approach to ecosystem management whereby ecosystem researchers actively engage conservationists and local resource users to make explicit, through open deliberation, the types of metaphors salient to their conservation problem.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".