Learning with Canadian Biosphere Reserves: Connecting researchers and practitioners through a national community of practice
Bibliographic record
Abstract
Invited presentation.Because of the complexity and uncertainty associated with efforts to achieve sustainability and transformational change, researchers have called for approaches that support deliberation, dialogue and systematic learning through reflection, evaluation and feedback among multiple participants.1 While research has focused on smaller-scale case studies, we do not know whether organizations that span spatial scales and governance responsibilities can establish effective communities of practice to facilitate learning and action.Additionally, while general principles of success such as shared vision, trust building and incentives have been identified, specific actions and factors supporting these principles have yet to be articulated.The purpose of this paper is to generate a framework that specifies actions and processes of a community of practice for collective learning and then to use the framework to assess a partnership established across a multi-level national network that included practitioners of 16 UNESCO biosphere reserves, and additional researchers and government representatives in Canada.
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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 | 0.040 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".