Arrow IFPA Series: Note 3 of 8: Public processes in sustainable forest management for the Arrow Forest District
Bibliographic record
Abstract
This extension note is the third in a series of eight that describes a set of tools and processes developed to support sustainable forest management planning and its pilot application in the Arrow Timber Supply Area (TSA). It summarizes the main public involvement processes used to obtain input to the Arrow Innovative Forest Practices Agreement (IFPA) Sustainability Project, contributing to the development and evaluation of criteria and indicators of sustainable forest management (SFM). This early public input guided the selection of criteria and indicators for the SFM pilot basecase analysis in the Lemon Landscape Unit.Sustainable forest management must be sustainable in a social sense and should incorporate public values. This extension note describes and evaluates several methods for involving the public in forest management planning. A standard mail survey was used to gather public perception data across a large geographic area (the former Arrow Forest District and the adjacent community of Nelson). Based on a systematic analysis of stakeholders in the IFPA area, a more focussed multi-criteria analysis (MCA) process was used to investigate stakeholder priorities and preferences for forest management scenarios at the landscape unit level. Although directed at different purposes and levels of detail, the survey and MCA processes identified some similar public values across a range of stakeholders. Both methods offer some advantages over more common public involvement processes used in British Columbia. To incorporate a broad range of public opinion, the use of multiple methods of evaluating public values is suggested in decision-making processes at various scales.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".