Building Common Ground: Complex Multi-party Governance of Forests in Northwest Ontario, Canada
Bibliographic record
Abstract
"The forests of Northwest Ontario, Canada are common property resources with an emerging and complex governance system involving industry and local, provincial, federal and First Nations governments. Matters are further complicated by recent shifts in the regional economy away from forest products. Additionally, movements towards inclusivity and collaboration have spurred several new partnerships for collaborative decision making respecting forests. In this context, the Common Ground Research Forum is investigating collaborative, cross-cultural governance and social learning in aid of sustainability. Our research within this forum aims to understand the complex, multiparty, cross-cultural governance systems that are developing in response to economic and societal transitions. Through the use of a learning approach to understanding complex partnership arrangements our paper explores how meaningful forms of collaboration have evolved, are maintained, and potentially affect the broader society, including reconciling past conflicts and wrongdoings in the Kenora region of Northwest Ontario. We focus on interconnected case studies that represent the movement toward collaboration. The cases involve the regional Grand Council of Treaty #3 First Nations, the Ontario Ministry of Natural Resources, a First Nations owned and operated resource management corporation, as well as a forest product company that is 49% industry owned and 51% First Nations owned. Narrative analyses of 32 interviews are used as a way of understanding learning platforms and learning outcomes for governing forest resources and enhancing cross-cultural, collaborative relationships. Results are presented as key findings about structural governance arrangements, as well as the rules, norms, and relationships that maintain them."
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".