Understanding climate change risk and vulnerability in northern forest-based communities
Bibliographic record
Abstract
Much research attention regarding climate change has been focused on the macrophysical and, to a lesser extent, the macrosocial features of this phenomenon. An important step in mitigation and adaptation will be to examine the ways that climate change risks manifest themselves in particular social localities. Certain social groups may be at greater risk, not solely because of their geographic location in a region of high climate sensitivity but also because of economic, political, and cultural characteristics. Combining the insights of economics and sociology, we provide an ideal-type model of northern forest-based communities that suggests that these communities may represent a particularized social context in regard to climate change. Although scientific research indicates that northern forest ecosystems are among those regions at greatest risk to the impacts of climate change, the social dimensions of these communities indicate both a limited community capacity and a limited potential to perceive climate change as a salient risk issue that warrants action. Five features of forest-based communities describe this context in further detail: (i) the constraints on adaptability in rural, resource-dependent communities to respond to risk in a proactive manner, (ii) the national and international identification of deforestation as a central causal mechanism in the political arena, (iii) the nature of commercial forestry investment planning and management decision-making, (iv) the potential by members of these communities to underestimate the risk associated with climate change, and (v) the multiplicity of climate change risk factors in forest-based communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".