The Interplay of Climate and Disaster in Men's Stories of the 2016 Kaikōura Earthquake in Aotearoa New Zealand<sup>1</sup>
Bibliographic record
Abstract
This paper contributes to the emerging field of men, masculinities, and disasters by drawing on narratives of men's accounts of the 2016 Kaikōura earthquake, including how stories of the earthquake intersect with experiences and understandings of extreme weather and climate change. A qualitative methodology was employed, and semi‐structured interviews were conducted with 19 men who experienced the 7.8 magnitude earthquake. This article offers an examination of the complexity of disaster experiences and recovery, as well as how people make sense of hazards and risks. We argue that ongoing exposure to climate hazards informed participant's responses to other infrequent natural hazard events, such as the Kaikōura earthquake. The research identified that men construct their own understandings and responses to natural hazards through a hierarchy of risk perception and probability based on personal experience.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".