The Canadian ‘Model Forest’ Approach: A Way Forward for Tasmania?
Bibliographic record
Abstract
Forest policy and forestry management in Tasmania have undergone a number of changes in the last thirty years, many explicitly aimed at improving industry sustainability, job security, and forest biodiversity conservation. Yet forestry remains a contentious issue in Tasmania, due to a number of interacting factors, most significant of which is the prevalence of a ‘command and control’ governance approach by policymakers and managers. New approaches such as multiple-stakeholder decision-making, adaptive management, and direct public participation in policymaking are needed. Such an approach has been attempted in Canada in the last decade, through the Canadian Model Forest Program, and may be suitable for Tasmania. This article seeks to describe what the Canadian Model Forest approach is, how it may be implemented in Tasmania, and what role it may play in the shift to a new forestry paradigm. Until such a paradigm shift occurs contentions and confrontations are likely to continue.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".