Integrating Multiple Knowledge Systems into Environmental Decision-making: Two Case Studies of Participatory Biodiversity Initiatives in Canada and their Implications for Conceptions of Education and Public Involvement
Bibliographic record
Abstract
Biodiversity initiatives have traditionally operated within a ‘science-first’ model of environmental decision-making. The model assumes a hierarchical relationship in which scientific knowledge is elevated above other knowledge systems. Consequently, other types of knowledge held by the public, such as traditional or lay knowledges, are undervalued and under-represented in biodiversity projects. Drawing upon two case studies of biodiversity initiatives in Canada, this paper looks at the role that constructivist conceptions of education play in the integration of alternative knowledge systems in environmental decision-making. In so doing, it argues that the conservation, sustainable use and equitable sharing goals outlined by the Convention on Biological Diversity (signed in 1992 under the auspices of the United Nations Environmental Programme) demand new models of governance which embrace the adaptive management qualities of learning organisations.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.038 | 0.022 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".