Towards New Institutional Arrangements for Managing Forest Commons in Northwestern Ontario
Bibliographic record
Abstract
"The forest industry has been the backbone of local economies in many remote locations in Canada. While this industry, which has focused on commodity products such as pulp, paper and lumber, thrived until the early part of this century, in recent years it has faced a major downturn that has resulted in extensive mill closures and unprecedented job losses to forest industry workers. Although municipalities that once benefited from the forest industry through employment and taxation are now experiencing negative social and economic impacts, Indigenous (First Nation) communities have generally been marginalized and historically received little benefit from the forest industry. This study examines the emergence of new institutional arrangements for the management of forest commons in northwestern Ontario (NWO) as an approach to improve the resilience of the communities that inhabit this vast boreal forest region. The study utilizes a qualitative approach based on semi-structured interviews with participants from 10 municipalities and 18 First Nation communities throughout NWO. The study participants include community leaders (mayors, chiefs, council) and key informants familiar with the forestry situation (former loggers and mill workers, lands and resources staff, and economic development officers). The study results have been used to formulate policy recommendations to develop a long-term economic vision to support sustainable local communities and the forest ecosystems that they depend on."
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".