Assisted tree migration in North America: policy legacies, enhanced forest policy integration and climate change adaptation
Bibliographic record
Abstract
The weight of much expert forest management opinion is that issues such as climate change can be effectively addressed only if forest policy-making moves from a purely sectoral focus and undergoes a shift to a more integrated multi-issue, multi-sector policy-making process. This is because credible adaptation policies in the sector require greatly enhanced multi-sectoral policy integration if they are to succeed. But this requirement may be beyond the capacity of many countries to deliver. This article explores the integration challenges faced by forest policy-making in Canada and the United States and uses the case of assisted tree migration to probe the reasons for the failure of institutions in both countries to develop and manage better vertical and horizontal integration in a climate change-related forest policy area. The article emphasizes the importance of previous rounds of policy-making or “policy legacies”, which serve to constrain contemporary policy options. It argues that due to the presence of many such legacies, forest policy development will continue to feature incremental adjustments through policy layering and policy drift, processes which limit the prospects for greater integration and better climate change adaptation in this sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".