Disentangling the social complexities of assisted migration through deliberative methods
Bibliographic record
Abstract
Abstract The impacts of climate change have prompted forest practitioners and decision makers to consider assisted migration (a form of plant translocation) as a management strategy. Historically, decisions around forest management, including the application of novel approaches, have been driven by the interests of particular groups and informed by a narrow range of knowledge inputs. Drawing on our program of social science research and that of others, we demonstrate: (a) the need to expand the range of groups consulted in climate‐adaptive forest management; (b) the necessity to incorporate a broader range of knowledge inputs; and (c) the development and application of deliberative approaches that facilitate both. Synthesis . We identify a novel deliberative agenda for understanding the societal aspects and implications of plant translocation research and practice, and make recommendations for mixed socially based research methods that revolve around engaging a diverse set of publics and forms of knowledge in environmental decision‐making and policy‐setting.
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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 | 0.160 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".