Learning from wildfire: co-creating knowledge using an intersectional feminist standpoint methodology
Bibliographic record
Abstract
Due to climate change, rural Canadian communities living in boreal regions can expect more intense and frequent wildfires. People’s experiences of wildfire hazards are differentiated by intersecting social factors such as age, gender, culture, and socio-economic status, as well as by social structures that enable or limit adaptation. This study engaged two Northern Saskatchewan communities in a process of co-developing a post-disaster learning framework and companion guidebook to support ongoing adaptation to climate hazards, enabled by the use of an intersectional feminist standpoint methodology. This methodology influenced both the process and outcomes of the research, which involved 18 interviews conducted with study community members and a workshop with a subset of the interview cohort. The intersectional feminist standpoint methodology facilitated insight into how intersecting social identity factors (e.g., gender, age, socio-economic status, and geography) shaped experiences of wildfire, as well as the need for and potential of post-disaster learning at the community level. In this paper, we focus on methodological insights for researchers and communities who seek to co-create knowledge and learning opportunities. In particular, we note the methodological impacts on research design choices, learning through the research process, and lessons learned through conducting community-engaged research during the early days of another kind of crisis: the COVID-19 pandemic.
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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 | 0.025 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".