One option, two countries, several strategies: subjacent mechanisms of assisted migration implementation in Canada and France
Bibliographic record
Abstract
Climate change obliges societies to develop adaptive strategies in order to maintain sustainable management of resources and landscapes. However, the development and implementation of these strategies require dialogue between researchers and policy‐makers about what they understand for adaptation. This dialogue can be hindered by language differences, the hidden agendas, and conflicting concerns of those involved. In this research study, we explored the mechanisms that underlie the implementation process of assisted migration ( AM ), an adaptation strategy that aims to limit the impact of climate change. We conducted a comparative analysis of 80 semistructured interviews with actors in the forestry sectors in Canada and France. In Canada, our results show a division between the provinces strategies, causing a debate about AM because researchers are wary of the geoengineering and economic arguments that frame AM in areas where the effects of climate change remain unclear. In contrast, we found that the observation of climate impacts is a strong trigger for the application of AM despite an awareness of its associated risks. In France, we explained the absence of AM implementation by a lack of information flow between research and foresters regarding the concept of AM , a cultural attachment of French foresters to their forest landscapes and that climate change effects are not clear yet. Clarity on what implies a true ecological engineering approach in ecological restoration can help maintaining adaptive actions like AM within the general scope of ecosystem management and minimize simplistic applications of adaptation strategies because of climate change.
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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 | 0.000 | 0.000 |
| 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".