Transdisciplinary science for strengthening forest systems in British Columbia: Quesnel as a learning landscape
Bibliographic record
Abstract
Forestry is becoming more complex as a result of diverging societal demands. Indigenous Peoples’ reconciliation and the challenges of climate change call for research that embraces transdisciplinarity, reciprocity, and problem-focused learning at the landscape scale. Both globally and in Canada, forestry and forest research are struggling to keep pace with this growing complexity. Place-based collaborative research and learning initiatives, described here as “learning landscapes,” offer an under-explored approach to meeting diversifying goals for forest landscapes. We describe recent progress in Quesnel, British Columbia, where researchers and local institutions are engaging to strengthen resilience and innovation in the forest sector. We first define the concept of learning landscape in the context of transdisciplinary sustainability science, and then illustrate this approach using the case study of Quesnel. We describe a process of systems diagnosis, including asset mapping and analysis of potential forestry pathways through a “best bets” framework. We propose a Theory of Change as a way forward, outlining opportunities for government, industry, and communities in developing regional capacity for integrated management and high-value forest products. We reflect on the contributions of learning landscapes to knowledge generation, experiential learning, and institutional development, and discuss implications for steering decision-making in locally driven sustainability transitions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".