Using perceptual maps to communicate concepts of Sustainable Forest Management – Collaborative research with the Office of the Wet'suwet'en Nation in British Columbia
Bibliographic record
Abstract
This article discusses collaborative research with the Office of the Wet'suwet'en Nation on their traditional territories in north-central British Columbia, Canada, a forest-dependent region where contemporary and traditional forest resources management regimes overlap. In-depth personal interviews with the hereditary chiefs and concept mapping were used to identify social-ecological linkages in Wet'suwet'en culture to inform the development of culturally sensitive social criteria and indicators of sustainable forest management (SFM) in this region. The preliminary results demonstrate how the CatPac II software tool can be applied to identify key component concepts and linkages in local definitions of SFM, and translate large volumes of (oral) qualitative data into manageable information resources for forest managers and decision-makers. Key words: social criteria and indicators, sustainable forest management, qualitative research, Wet'suwet'en
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".