Analyzing Forest Policy to Advance Indigenous-Led Forestry Initiatives and Increase Adaptive Capacity
Bibliographic record
Abstract
Indigenous groups across Canada continue to regain sovereignty over their traditional territories and this research focuses on their involvement in Manitoba’s forest sector. A large proportion of First Nations communities in Manitoba are forest-based, and there is a revitalized opportunity and vigor for communities to build successful and sustainable forestry initiatives that could address their respective goals while building adaptive capacity towards climate change impacts. The focus of this research was to understand the barriers and opportunities Indigenous groups experience in respect to federal and provincial forest policy and how Indigenous-led forestry initiatives can enhance the adaptive capacity and climate change resilience in First Nation communities. The first research objective was to describe federal, provincial, and Indigenous policy measures impacting Indigenous-led forestry. This was achieved through a systematic policy scan and interviews with Indigenous forestry experts that uncovered various impactful measures, including enabling legislation and preventative legislation. The second objective was to identify policy provisions that could support or hinder Indigenous-led forestry. The results show that while Indigenous groups are often excluded from forest policies and policy making processes, the provincial and federal governments have increased efforts towards Indigenous inclusion in recent years. A notable example is the progressive timber harvesting agreement that was negotiated between the provincial government and Norway House Cree Nation in 2022. The third objective aimed to identify opportunities for policy learning about Indigenous-led forestry. Indigenous inclusion in policy making could lead to greater learning opportunities and this research demonstrates there are increased opportunities for policy learning to occur in Manitoba’s forest sector. The final objective was to develop recommendations for improving the prospects for Indigenous-led forestry based on accrued evidence and consultation with First Nations communities. While recent strides have been made in Manitoba in advancing Indigenous participation in the forest sector, the wood supply surrounding many First Nations remains underutilized. Moving forward, the success of Indigenous-led forestry initiatives will hinge on increased collaboration with governments and industry, provincial reform of forestry legislation that does not explicitly address Indigenous rights and interests, and funding programs that could address the economic and logistical barriers associated with developing a local forestry initiative. Indigenous-led forestry initiatives that seek to advance the unique goals of individual First Nations remain limited in Manitoba, and this research hopes to help address this gap.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".