The vulnerability of Canadian forest-dependent communities to climate change: an indicator-based approach
Bibliographic record
Abstract
In Canada, recent forest disturbance episodes such as insect outbreaks, forest fires, and drought have significant consequences on forest ecosystems while future climate change is expected to result in more severe impacts. These effects of climate change will impact the communities that depend on forests for their livelihood. Effective responses from forest-dependent communities require a comprehensive understanding of climate change impacts and of adaptive capacity assets at the local scale. Vulnerability assessments documenting sensitivity, exposure, and adaptive capacity dimensions have the potential to fill that baseline information gap. This article uses an indicator-based approach to describe the vulnerability to climate change of Canadian forest-dependent communities. We used indicators derived from the 2016 Canadian census to describe the vulnerability of 2270 census subdivisions: 6 indicators were developed for sensitivity, 14 for exposure, and 27 for adaptive capacity. Hierarchical clustering was carried out to distinguish archetypes for each vulnerability dimension. Groupings confirmed that communities experience different vulnerability types across the country. Mapping of vulnerability types showed a strong longitudinal effect of exposure while regionally, it varies more latitudinally due to variation in sensitivity and adaptive capacity. The approach provides relevant baseline information for crafting adaptation strategies tailored to the characteristics of forest-dependent communities.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".