Engaging Multiple Disciplines in Ecosystem Services Research and Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it