Engaging Multiple Disciplines in Ecosystem Services Research and Assessment
Bibliographic record
Abstract
We thank Orenstein for discussing our recent article in BioScience ( Raymond et al. 2013 ). His central argument is that social scientists need to be better engaged in ES assessment if the concept is to be mainstreamed into policy and practice. We agree. Along those lines, we called for a deliberative approach to ecosystem management that actively engages multiple stakeholder groups in meaningful dialogues in order to understand the ways that people relate to nature before adopting a specific metaphor a priori to portray human—environment interactions. Such a deliberative approach requires an interdisciplinary approach to ES assessment. Our article, which was the result of a workshop that invited a broad suite of social scientists (many new to the concept of ecosystem services) to think seriously about what their disciplines and methods could offer to the study of cultural values and social change in ecosystem services. Furthermore, many of the authors of this article are trained in the social sciences. We therefore extend Orenstein's argument in that social and natural scientists of all stripes have an important role in navigating the policy process, in providing relevant social and ecological data for policymakers, for the communication of results, and for stakeholder integration.
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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 | 0.130 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".