Vulnerability and adaptation to climate change in Indigenous communities in Canada and Guatemala : the role of social capital
Bibliographic record
Abstract
The burning of fossil fuels and other human activities generating GHG are causing global warming. Global warming impacts such as droughts and floods are not uniform, and societies that are most vulnerable will be affected most. Indigenous communities are more vulnerable because they face more challenging socio-economic and environmental conditions compared to the dominant societies that surround them. However, some indigenous communities have developed strategies that enable them to adapt to climate change. Some of these adaptation strategies include the sustainable management of resources, diverse sources of income and the maintenance and reliance on social support systems–social capital. Some indigenous communities utilize networks of social support that allow them to influence their social, economic, political and environmental conditions. These networks of social support can also be utilized for the flow of information and to disseminate strategies that lead to collective action required to address the various stresses that they face. This study investigated the importance of social capital in adaptation to impacts of climate change. Two indigenous communities with different forms of livelihood: the Blood Tribe, in Canada, and the town of Patzún, in Guatemala were compared and contrasted. Understanding the role of social capital in adaptations to climate change impacts can provide adaptation insights to other indigenous communities and other vulnerable sectors. The change from a subsistence livelihood tends to reduce the social capital of these communities. In Canada, indigenous communities’ dependence on commercial activities and/or government support reflects the dramatic change from an earlier subsistence livelihood. In the highlands of Guatemala, most communities still maintain their subsistence livelihood, though it is increasingly being integrated into a market economy. The results of the investigation project show that the community of Patzún has more diverse livelihood strategies and stronger social capital compared to the Blood Tribe. The community of Patzún has a larger capacity, and therefore more options to adapt to climate change. This conclusion has implications for the current discussions on change and direction required to enhance the adaptive capacity of indigenous people and the factors that hinder their adaptation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".