Adapting forest ecosystems to climate change by identifying the range of acceptable human interventions in western Canada
Bibliographic record
Abstract
Forest management is presently undergoing major changes to adapt to climate change. This research examines the variation in perceived acceptability of potential forest management interventions that can mitigate the risks of climate change among rural forest-based communities in British Columbia and Alberta. In each of the four study communities, three focus groups composed of foresters, environmentalists, and local citizens were consulted. A Q-sort exercise was utilized to measure the perceived acceptance of a set of nine forest adaptation management scenarios that represented a spectrum of human interventions in forested ecosystems. The theory of Cultural Cognition of Risk was applied as a theoretical framework to analyze the way in which participants perceived adaptation strategies. Results indicate that foresters perceived the strategies based on assisted migration as being relatively less acceptable compared with the other social groups, while environmentalists prioritized adaptation strategies that featured mixed species, and local citizens perceived all of the adaptation strategies more neutrally. Cultural Cognition of Risk theory was determined to play a role in shaping perceptions of the adaptation strategies in that individualists tended to accept the local-based strategies while opposing the assisted migration based strategies. Conversely, hierarchists perceived assisted migration based strategies more favourably than the other cultural groups.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".