Using a causal conceptual model of managed dyke realignment as a boundary object promotes multi-stakeholder collaboration and co-production
Bibliographic record
Abstract
Collaboration in cross-sectoral initiatives with diverse groups of stakeholders can be facilitated using boundary objects. Knowledge co-production based in such collaboration approaches can contribute to solve pressing environmental issues. In this paper, we describe the process of developing a conceptual model through synthesis and expert knowledge elicitation to assess social-ecological dynamics in the context of managed dyke realignment in a complex coastal landscape around the Bay of Fundy, Canada. We explored how the model served as a boundary object for multi-stakeholder collaboration, and how it contributed to interdisciplinary research within our case study. A participatory approach involving stakeholders from different institutions and backgrounds who are actively working in the landscape, was carried out through 5 elicitation phases in 5 months. This participatory process produced a conceptual model that meets the characteristics of a boundary object and contributes to the principles of co-production. Discussions about model functionality and the complexity of the system, including conflicting arguments, emerged from the participatory process. The process highlights climate adaptation policy implications such as the need to decrease administrative complexity and facilitate funding access, as well as guarantee long-term monitoring of implementation sites to pursue adaptive management. We suggest the model structure and process presented in this work can be used to assess other management strategies in this and similar landscapes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".