Seasons of Stress: Understanding the Dynamic Nature of People’s Ability to Respond to Change and Surprise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Climate change is impacting coastal communities in rural Alaska in multiple direct and indirect ways. Here, findings are reported from ethnographic research done with municipal workers, community leaders, and other local experts in the Bristol Bay region of Alaska, where it is found that climate change is interacting with local social and environmental circumstances in ways more nuanced than are generally captured by frameworks for vulnerability analysis. Specifically, the research herein shows the importance of the temporal dimension of vulnerability to environmental change in rural Alaska, both in terms of temporal patterns that emerge from climate-driven stressors and also with respect to how, and under what conditions, people in rural communities may design or manage effective responses to change. There are multiple factors that play into how rural communities will be affected by some climatic or environmental stress; ultimately, the impacts of climatic and environmental stressors will differ depending on where, when, and how frequently they occur. To capture these interactions, two analytical concepts—community capacity and cumulative effects—are discussed and then incorporated into a visual tool for improved planning and vulnerability analysis.
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.
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 | 0.001 | 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 | 0.001 | 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 it