Exploring the role of boundary work in a social-ecological synthesis initiative
Bibliographic record
Abstract
Abstract Inter- and transdisciplinary collaboration in environmental studies faces the challenge of communicating across disciplines to reach a common understanding of scientific problems and solutions in a changing world. One way to address current pressing environmental challenges is to employ a boundary work approach that uses activities across borders of separated field of research. But how can this look like in practice? In this research brief, we self-evaluated the boundary work approach in a synthesis group on socio-ecological systems, based on an online survey with participants. Here, we discuss how boundary work can be used to integrate the knowledge from natural and social scientists both working on social-ecological systems. We found participants were selected to be acted as boundary spanners and were willing to cooperate for solving multidisciplinary issues regarding the understanding, management, and maintenance of ecosystem services. A social-ecological network analysis framework served as a boundary concept and object for communication and knowledge integration. Being familiar with a joint boundary concept like ecosystem services prior to the working group event supported the communication of participants. These results indicate that synthesis initiatives could strategically leverage boundary work through the careful selection of members, with the inclusion of boundary spanners, as well as prior joint identification of boundary concepts and objects.
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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 | 0.128 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".