North American Forest Futures 2018–2090: Scenarios for Building a More Resilient Forest Sector
Bibliographic record
Abstract
North American forests and forest management institutions are experiencing a wide range of significant ecological disturbances and socioeconomic changes, which point to the need for enhanced resilience. A critical capacity for resilience in institutions is strategic foresight. This article reports on a project of the North American Forest Commission to use Futures Research to enhance the resilience of forest management institutions in North America. The Aspirational Futures Method was used to develop four alternative scenarios for the future of North American forests and forestry agencies: (1) an extrapolation of current trends into the expectable future titled Stressed Forests, (2) a scenario of growing desperation titled Megadisturbances Call for Military Intervention, (3) a high aspiration future titled High Tech Transformation and Cooperation, and (4) an alternative pathway to a highly preferable future titled Cultural Transformation Embraces Indigenous Values. These scenarios will be used in discussions and futures exercises with forestry leaders to develop foresight and assure that plans are responsive to the challenges and opportunities ahead.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".