Adaptive capacity for climate change in Canadian rural communities
Bibliographic record
Abstract
It is widely acknowledged that promoting the long-term sustainability of rural areas requires an assessment of their capacity to handle stress from a host of external and internal factors such as resource depletion, global trading agreements, service reductions and changing demographics, to name but some. The sustainability literature includes a number of approaches for conducting capacity evaluations but is sparse regarding effective methods and empirical examples. This article provides one approach for assessing community capacity and gives results from its application to a specific Canadian rural community. The authors use general capacity variables and indicators to focus on a particular stress, namely impacts from climate change, and on one type of capacity, namely the capacity to adapt (to such climatic change). A basic framework and profiling tool (‘amoeba’) for describing the resources underlying community adaptive capacity are offered. The researchers provide a set of indicators reflecting social, human, institutional, natural and economic resources and relate them to climate change adaptation at the community level. Although the indicators cannot be replicated exactly for other rural communities, the essentials of the framework and the profiling tool can. In fact it is hoped that the ideas and example found in this article will encourage researchers to enhance and improve on the methods and results for work on community capacity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".