Frequent Flooding and Perceived Adaptive Capacity of Subarctic Kashechewan First Nation, Canada
Bibliographic record
Abstract
Perceived (socio-cognitive) capacity is as important as objective (material resources) capacity in assessing the overall adaptive capacity of people at the community level. Higher perceived and objective capacities generate greater total adaptive capacity. This article assesses the perceived adaptive capacity of the Kashechewan First Nation, located in the flood-prone southwestern James Bay (Subarctic) region in Canada. The community is frequently disrupted by the elevated risk of spring flooding and has experienced five major floods since its establishment in 1957. Residents have been evacuated 14 times since 2004 because of actual flooding or flooding risk and potential dike failure. We surveyed 90 residents using 21 indicators to assess the community’s perceived adaptive capacity. The results indicate that residents’ risk perception and perceived adaptive capacity are high and are reshaping their adaptive behavior to the hazard of spring flooding. The strong positive interrelationships between human capital, social capital, governance, and other determinants, such as migration, personal resilience, and experience, also suggest high perceived adaptive capacity. Human capital and the other determinants are relatively higher contributors to the perceived adaptive capacity, followed by social capital and governance determinants. The results also indicate that residents’ disaster preparedness has also improved. The elevated flooding risk and frequently occurring emergencies have motivated the First Nation to modify their spontaneous and proactive adaptation responses for disaster risk reduction at the individual, household, and band levels. Planning to adapt to natural hazards to mitigate their impacts also requires a nuanced understanding of the perceived adaptive capacity that contributes to overall adaptive capacity. Translating the high perceived adaptive capacity into greater total adaptive capacity would contribute to enhancing community resilience.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".