The importance of context-relevant feminist perspectives in disaster studies. The case of a research on forest fires with the Atikamekw First Nation
Bibliographic record
Abstract
Purpose My research is a study of forest fires that occurred near the Atikamekw community of Wemotaci (Quebec, Canada). This article focuses on the gendered aspects of two forest fire situations experienced by the people of Wemotaci, as I realized during fieldwork that men and women had different experiences and roles during the fires that did not seem to be valued the same. As a result, I decided to mobilize Indigenous feminist theories to understand the entanglement of multiple oppressions especially colonialism and the patriarchy in disaster situations. Design/methodology/approach I used interviews, participant observation and focus-groups during several stays in Wemotaci. I drew on methodologies developed by Indigenous researchers who aim to decolonize research. In this approach, I built respectful relationships with participants, conscious that I was part of the network I was studying. Findings This research reveals the importance in disaster research to adapt our methodology to the participants realities while factoring our positionality in. More specifically, I show how the use of an Indigenous feminist perspective allows me to understand how patriarchal-colonialism manifests during forest fire situations intertwined with traditional Atikamekw gender roles. This understanding makes it possible to see ways of managing and studying disasters that challenge systemic oppressions by rethinking the notion of vulnerability and making space for Indigenous people agency, knowledge and experiences. Originality/value The use of a feminist framework in this male-dominated field is still innovative, especially mobilizing a feminist approach that is consistent with the participants' realities while acknowledging the researcher's positionality which translate here in the use of Indigenous feminist theories.
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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 | 0.002 | 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 | 0.001 |
| 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".