Why we disagree about assisted migration: Ethical implications of a key debate regarding the future of Canada's forests
Bibliographic record
Abstract
Assisted migration has been proposed as one tool to reduce some of the negative ecological consequences of climate change. The idea is to move species to locations that could better suit them climatically in the future. Although humanmediated movements are not a recent phenomenon, assisted migration has lately been the source of debate, in particular within conservation biology circles. In this paper, we outline the major perspectives that help define differing views on assisted migration and shed some light on the ethical roots of the debate in the context of Canadian forests. We emphasize that there are many different forms of assisted migration, each responding to different (often unstated) objectives and involving unique risks and benefits, thus making the debate more nuanced than often portrayed. We point out certain seeming contradictions whereby the same argument may be used to both support and oppose assisted migration. The current debate on assisted migration primarily focuses on ecological risks and benefits; however, numerous uncertainties reduce our capacity to quantitatively assess these outcomes. In fact, much of the debate can be traced back to fundamental perspectives on nature, particularly to the ethical question of whether to deliberately manage natural systems or allow them to adapt on their own. To facilitate discussion, we suggest that the focus should move towards a clearer identification of values and objectives for assisted migration.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.049 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 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".