Crossing the Divide: Engaging scientists and policy-makers in adapting forest management to climate change in British Columbia
Bibliographic record
Abstract
The Future Forest Ecosystems Scientific Council (FFESC) was created in 2008 following a one-time allocation of funding from the BC provincial government to support research that would inform adaptation of BC’s current forest management policies to a changing climate. A key goal of the council was to maximize the utility of the research to inform provincial policy. The eightstep process that we developed to achieve this goal is described in this paper. In roughly chronological order, the eight steps were: determining the research needed to inform policy, connecting scientists and policy-makers, requiring interdisciplinary teams including both natural and social scientists and relevant stakeholders, assessing proposals for their value to inform policy, fostering scientific excellence, fostering ongoing communication between scientists and policy-makers, tailoring communication to policy-makers, and disseminating the policy-relevant outcomes in a timely and targeted manner. Based on the FFESC experience, we suggest best practices for engaging policy-makers in research and scientists in policy development and adaptation.
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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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.066 | 0.017 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".