Business ecosystems and sustainability transitions in forest landscapes: A case study in British Columbia, Canada
Bibliographic record
Abstract
Abstract Empirical understanding of sustainability transitions in forestry offers unexplored and important insights into how change happens in forest landscapes. In British Columbia, Canada, the provincial government is taking steps to transform the way forests contribute to societal, environmental, and economic value. Policy reforms aim to support local stewardship and enterprises for a more diverse , competitive , and innovative forest sector. We explore the ability and agency of landscape actors to realize this change. Focusing on the Quesnel forest landscape, we use a business ecosystem framework to identify collaboration networks and value sharing mechanisms at the landscape-scale. Results from key informant interviews indicate that high-volume, reliable, efficient, integrated, low-cost wood products remain the primary value-proposition of the Quesnel forestry network. However, shared values are shifting towards resilience, reconciliation, and partnerships. Regional forestry actors are highly interdependent, yielding many economic and environmental benefits but reducing the network’s dynamic ability to deliver upon these diversified values. Provincial policy is creating space for regional actors to drive transformative change but diminished agency, characterized by uncertainty and disempowered leadership, is hindering action. Our findings suggest that shared values of business ecosystems can mobilize agency and shift value-propositions towards sustainability transitions, but progress may be limited by deep systemic barriers. Continued progress towards BC’s sustainability transition may require intermediate steps focused on aligning values, restoring trust, and clearing regulatory obstacles for long-term change. Wildfire resiliency is emerging as an opportunity space for leveraging the strengths of the Quesnel forestry network, reconciling local and provincial objectives, and shaping forestry systems that are reflective of more inclusive value-sets. Carefully navigated, these pathways can support improvements in the public’s understanding and perception of forestry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".