Building common ground: learning and reconciliation for the shared governance of forest land in northwestern Ontario
Bibliographic record
Abstract
Historically, First Nations in Canada have been marginalized with regards to the governance of forests. This has contributed to racial tensions in places such as northwestern Ontario, where First Nations and settlers have come into conflict over land allocations and forestry practices, causing a great need for reconciliation within the institutions built around forests. Recent socio-economic shifts have influenced forest tenure reform in Ontario, and in the northwest this has contributed in part to examples of collaborative forest governance involving First Nations, as well as First Nations involvement in the business side of forest management. This research used a case study approach and considered two interconnected case studies. The first is Wincrief Forestry Products Ltd., a forest products company that is 49% industry owned and 51% First Nations owned. The second is the Miitigoog General Partner Inc., a larger collaborative organization (inclusive of the first) that was set up to manage the Kenora Forest through a Sustainable Forestry License with decision-making authority shared equally by First Nation and industry partners. The purpose of my research was to understand the implications of transformative learning within cross-cultural settings, particularly how such learning can inform collaborative governance of shared land and resources. Qualitative methods were used, including document review and semi-structured interviews with key informants and those involved in governance [n=43]. Data related to governance were analyzed using institutional mapping, and other data were coded according to the learning and transitional justice literature. Findings are presented as learning outcomes and processes, and contextualized forms of learning relating to cross-cultural collaboration. The research makes several contributions to understanding governance, learning and reconciliation within the context of cross-cultural forest management. Key results included evidence of the importance of informal learning and learning together prior to formal collaboration. Another key result was that learning outcomes may depend on when partners enter the collaboration, and in relation to this that working through initial conflicts were an important aspect of learning through collaboration. This research highlights the importance of learning-by-doing, connections between culture and learning, and the importance of “two-row” (non-assimilative) and decolonizing approaches to understanding cross-cultural learning.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| 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".