Exploring the Links Between Individual and Social Learning in the Red River Floodway Environmental Assessment
Bibliographic record
Abstract
In general, top-down expert-driven resource management approaches have proven to be ineffective when applied to problems that embody a high degree of complexity, uncertainty, and conflict. In Canada, and elsewhere in the world, there is a heightened level of interest in alternative resource management strategies and practices. The incorporation of meaningful public involvement and social learning opportunities is particularly important in resource management situations that run the risk of affecting various stakeholders. This research investigated the linkages between individual and social learning in the context of public involvement in environmental assessment (EA). The Red River Floodway EA provided an appropriate setting to investigate this issue in the general context of management for sustainability. Effective public involvement in EA can ensure that the project outcome reflects the values and interests of the communities involved. Two groups were identified based on their involvement in the Floodway expansion EA. The Coalition for Flood Protection North of the Floodway (CFPN) is loosely structured, informal, and not very well funded. The Cooks Creek Conservation District (CCCD) is reasonably funded, highly formal, and well connected to the municipal and provincial governments. The research methods of this study were guided by the assumptions of a qualitative case-study approach. Face-to-face interviews, using open-ended questions, were the primary source of data. Public involvement in EA provides an excellent opportunity for community organizations to engage in social learning about resource management activities that may potentially affect the natural environment and surrounding communities. Several factors contributed to the social learning outcomes that were achieved by the CFPN and CCCD. Both groups were transparent in their decision-making and idea-sharing processes. Furthermore, both the CFPN and CCCD possessed strong leadership within their organizations. Finally, both groups effectively documented their activities and provided opportunities for members to engage in dialogue throughout the EA process. The evidence from the study suggests that doing an analysis of organizations participating in EA adds value and understanding to public involvement and how it is structured. It also adds value to understanding the communications and dynamics of groups participating in public involvement processes. Furthermore, this research recognizes the importance of identifying and addressing possible impediments to social learning in community organizations. Organizations that engage in social learning effectively will be capable of making informed decisions which may contribute to their success in public involvement forums.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".