The Psycho-Anthropological Perspectives of Natural Hazards: Applicability of the ‘Protection Motivation Theory’ in Explaining Behavioral Responses Towards Tropical Cyclone Idai in the Chimanimani District of Zimbabwe
Bibliographic record
Abstract
This paper adopts a psycho-anthropological approach to explain individual behaviors in response to tropical cyclone Idai which made a landfall in the Chimanimani district of Zimbabwe in March 2019. Employing the Protection Motivation Theory (PMT) as a lever of diagnosis, the study sought to demonstrate how psychological concepts and anthropological approaches can be infused to improve disaster preparedness. The evidence presented in the paper is based on an intensive ethnographic study conducted in Chimanimani district between November 2020 and July 2021, and which benefited from a variety of data collection techniques. The research findings reveal that beyond its utility in predicting individual protective behaviors towards a disaster, the PMT framework can be adopted as a tool with which postmortems of past disasters can be conducted to identify gaps and inform future disaster administration. The findings suggest that to be useful as a policy making and planning tool, the PMT should remain flexible, allowing for modifications to suite different socio-cultural contexts, including the flexibility to incorporate salient factors that might influence individuals’ cognitive mediating processes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".