Business ecosystems to provide incentives and opportunities for sustainable and resilient livelihoods in forest landscapes
Bibliographic record
Abstract
Initiatives to strengthen small-scale forestry have proliferated in the recent decades. Existing literature has identified multiple factors that may hinder or improve the adaptive capacity of small-scale forestry, considering small-scale operations or business activities as an alternative to the large-scale industrial model that has long dominated the world’s forests. More recent research on business systems and strategies suggest a need to employ systems thinking, or business ecosystem approaches, to decipher complex relationships between different types and sizes of businesses rather than focusing on a specific business type or size. This thesis addresses this knowledge gap by combining insights from business literature and practices, and previous studies on sustainable and resilient livelihoods. Case studies of British Columbia, Canada, and Maluku Province, Indonesia were investigated to understand how business ecosystems unfolded in forest landscapes with different ecological and socio-economic backgrounds. The study in British Columbia focused on a local forest initiative created by the City of Quesnel to encourage innovation and improve the resilience of the local forest industry. The data was collected through interviews with government officials, non-governmental organizations, tertiary education institutions, and industry actors and applied actor network analysis methods to examine the role of different forest actors in the knowledge and business networks. The study in Indonesia investigated the way in which local communities in two villages on Seram Island, in Eastern Indonesia, used business activities to improve their livelihoods and adapt to their changing landscapes. Government regulations and previous participatory appraisal data obtained by non-governmental organizations were used to identify business network and landscape conditions that influence the operation of small-scale businesses and tenure holders. The findings reveal that business ecosystems in British Columbia and Indonesia are shaped by policy frameworks concerning land and tenure rights, which influence the dynamics of business and knowledge networks. This thesis highlights the importance of analysing how the underpinning policy framework affects the role and positions of each actor in their respective business ecosystem. The findings of this thesis suggest further research on the application of the business ecosystem framework to achieve sustainable and resilient livelihoods in forest landscapes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".