Differences in Nature Based Solutions perception and implementation strategies across academic disciplines, an empirical analysis
Bibliographic record
Abstract
This study investigates using a survey how disciplinary scholars perceive Nature Based Solutions (NBS) and how they differ in their NBS implementation approach at the local level. Respondents participated in the 2020-2021 , a ten-week course (online from Dec. 3, 2020, to Jan. 26, 2021) with a focus on Disaster Risk Reduction and Water Security. Supported by the United Nations Environmental Program and the Partnership for Environment and Disaster Risk Reduction (PEDRR), a global alliance of UN agencies, NGOs, and institutes, the Winter School Program is delivered via a partnership model between the University of Massachusetts Amherst's School of Public Policy and Department of Economics, McMaster University, and the United Nations University. Aiming to build young professionals' capacity on NBS framing and application potential, the Program focuses on the delivery of conceptual and empirical information on ecosystem-based climate adaptation and disaster risk reduction. The Program represents a knowledge hub and an opportunity to network with scholars, international experts, and practitioners. 40 graduate students from numerous disciplines (e.g. economics, public policy, international affairs, geosciences, engineering, chemistry and physics) have been selected to attend the Program and have participated in a survey to assess how disciplinary scholars perceive NBS and to explore differences in strategies and priorities while implementing NBS within communities. The results of the survey offer lessons about opportunities and possible challenges of interdisciplinary collaborations when implementing NBS.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".