Public engagement in forest governance in Canada: whose values are being represented anyway?
Bibliographic record
Abstract
Researchers and advocates have long argued that on-going engagement by broad segments of the public can help make forests and forest-based communities more sustainable and decisions more enduring. In Canada, public engagement in sustainable forest management has primarily taken one of two approaches: advisory forums through forest-sector advisory committees (FACs) and direct decision-making authority through community forest boards (CFBs). The purpose of this paper is to compare these two approaches by focusing on who participates and the values that participants bring to their deliberations. We conducted a national survey of FACs and CFBs involving 402 participants. Results showed that both models favoured well-educated, Caucasian men and fell short on the representation of women and Indigenous peoples. Additionally, despite different levels of authority in relation to forest management decisions, participants in CFBs and FACs shared similar forest values. Hence, we conclude that neither model of forest governance encourages participation from a diverse public. Our findings suggest the need to find new ways of recruiting diverse participants and to investigate more deeply whether local and extra-local pressures and power dynamics shape these processes. Such information can inform the establishment of more robust institutions for decision-making in support of sustainable forest management.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".