“Are you prepared or not?”: An intersectional analysis of a community-engaged climate change vulnerability assessment and adaptation planning process with Tsáá? Ché Ne Dane
Bibliographic record
Abstract
Intersectional analyses of climate hazards have demonstrated that social dimensions play important roles in how people experience and respond to climate change and extreme weather events. Despite these insights, intersectional scholarship has faced criticism around its theoretical orientation and the resulting challenges of doing applied intersectional research to understand social dimensions of climate change. This article demonstrates the value of an intersectional feminist lens to community-level planning for climate change. Working with an Indigenous community in northern British Columbia, Canada, the research revealed that social dimensions including culture, age, gender, and spirituality combined in distinct and various ways to influence how the community framed the problem of climate change, expressed agency, understood impacts and vulnerability, and proposed responses. Attending to these dimensions throughout a community-engaged climate change vulnerability assessment and adaptation planning process illuminated differences among groups, while also exposing shared goals and areas of overlap among diverse perspectives and worldviews. Beyond exposing commonalities, consistent consideration of social dimensions also enhanced local adaptive capacity and shaped the planning and decision-making process by informing project framing and design, methods selection and participant recruitment, and developing meaningful outputs. We use this evidence to demonstrate the practical application of an intersectional lens and to explain how embedding consideration of social dimensions within climate change vulnerability assessment and adaptation planning processes can produce better contextualization, greater buy-in, and more meaningful outcomes for communities across Canada and beyond. • We apply an intersectional feminist lens to community-level climate change planning. • Key relevant social dimensions included culture, age, gender, and spirituality. • Attention to social dimensions helps build local adaptive capacity. • We offer an example of how intersectionality theory can be applied in practice.
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 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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".