Local impacts of federal forest policy changes on Canadian model forests: An institutional capacity perspective
Bibliographic record
Abstract
Abstract Although research in multiparty environmental governance has examined how local actors work together, few have focused how changes in higher order government policy directives affect the capacity of local organizations to implement associated management activities over time. We examine changes in three Canadian Model Forests as federal policy objectives shifted from “sustainable forest management” to “sustaining communities.” Specially, we adopt the concept of institutional capacity from planning theory to assess changes in knowledge resources, relational resources, and mobilization potential of Model Forest sites during the shift from the Model Forest Programme to the Forest Communities Programme. Analysis of key documents shows that despite being developed as a top‐down programme, individual sites exhibited an array of responses by drawing on local actors with new skills, political acumen, and relational resources to generate local opportunities. Although overall federal support decreased, Model Forest sites fostered collaborations with new sectors, enabling them to link ideas, resources, and influence in new ways and respond to changes they observed in the local context. Local networks created under a federal programme were able to move forward, shift their organizational identity, change visions, and initiate alternative projects after the programme stopped.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".