Gender-Based Experiences and Perceptions after the 2010 Winter Storms in Atlantic Canada
Bibliographic record
Abstract
This paper conveys the findings of the first phase of a longitudinal study into climate change adaptation in Atlantic Canada. Men and women from 10 coastal communities in three provinces (Quebec, New Brunswick, and Prince Edward Island) were interviewed to better understand how both sexes perceived and reacted to extreme weather events. Their responses were recorded based on their experiences, personal and community levels of preparedness, as well as help received and effects on their lives. Most importantly, the findings denote that more men were personally prepared and more active in the community than women. More men recognized a deficiency in help at the community level, and were critical of government in particular, addressing a lack of financial interventions and support. Women were forthcoming with their emotions, admitting to feeling fear and worry, and their perceptions in terms of impacts and actions were closer to home. The results support what others have shown that in rural and coastal communities the traditional division of labor may influence and lead to a gender bias in terms of actions and gradual adaptation in communities. There is a need to better understand how these sometimes subtle differences may affect decisions that do not always consider women's roles and experiences in the face of extreme events.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".