A policy scan related to assisted migration as a climate change adaptation tactic in Canada reveals major policy gaps
Bibliographic record
Abstract
Assisted migration is increasingly being considered as a potential climate change adaptation tactic even though it also comes with potential risk to ecosystems and society. When implementing conservation actions that involve risk, it is prudent to have policies and guidelines to ensure that such actions are conducted in ways that conform to regional standards and consider risks. Here, we report on a policy scan focused on assisted migration in the context of climate change adaptation originally as a protected areas tactic only, but then broadened to ecosystems in Canada beyond those boundaries. Policy scans are a useful strategy for understanding the evolving policy and regulatory landscape for a given topic and can guide the development of such policies in other jurisdictions. Our scan focused on Canada, where multi-scalar governance systems exist relevant to biodiversity and environmental management. Our comprehensive policy scan (involving scans of legislation, policies, and guidelines found online and through direct inquiries with government bodies) revealed major gaps in the assisted migration policy landscape with very few provincial/territorial or federal policies in Canada. A more rudimentary scan in the United States revealed a similar pattern. There was evidence that some jurisdictions anticipated need for such policies and even a few examples of very specific policies (e.g., seeds) that had already been developed, but there were not comprehensive policies or frameworks. Governments and other relevant bodies/organizations may wish to consider working collaboratively toward the development of robust, evidence-based policies for assisted migration given that we anticipate this conservation intervention becoming more popular as climate change impacts on ecosystems become more evident and dire.
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 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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".