Creating, Sustaining, and Improving Collaboration Across Canadian Biospheres Through the Lens of Collective Impact Theory
Bibliographic record
Abstract
Natural resource management agencies rely on collaboration to support their missions. UNESCO Biosphere Reserves/Regions (BRs) are cutting-edge models for conservation reliant on collaboration. We use Collective Impact Theory (CIT) to help understand the complexities of inter-BR collaboration. We conducted semi-structured interviews with representatives from 14 of the 19 Canadian BRs, and used thematic analysis to explore the benefits, barriers, enablers, and best practices associated with collaboration. Respondents emphasized that adequate organizational and individual capacity enabled collaboration, and the Canadian Biosphere Region Association helped connect BRs beyond personal relationships. Respondents stressed inclusion and accessibility in collaboration, particularly opportunities for Indigenous Peoples. Key enablers and barriers of collaboration included goal alignment, awareness and accessibility, and trust. Using CIT, Canadian BRs demonstrated great potential for organized and inclusive collaboration. These findings can help organizations develop inclusive collaborative opportunities and shed light on collaborative theory in general.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".