Risk Perception in a Developing Country: The Case of Jordan
Bibliographic record
Abstract
Purpose: Recognizing the importance of risk perception and the poor understanding of the phenomenon in developing countries, this study characterizes risk perception in Jordan. Design: A mixed quantitative and qualitative survey approach was adopted. Semi-structured interviews were used to identify risks relevant to Jordanian society, then perceptions of these were examined using a questionnaire. Sampling was stratified by province and convenience sampling was adopted within each stratum. The response rate of 53.6 percent was adequate for accurate and useful results representative of the target population. The questionnaire data were analyzed using parametric statistics including principal component analysis and mean analysis. Findings: A cognitive map shows that Jordanians perceived warfare and terrorist attack as the most dreaded, catastrophic and uncontrollable risks to society. Refugee influx was also perceived as a high risk on the dreaded dimension. Although Jordan generates no nuclear electricity, nuclear power was also loaded high on the dreaded factor, confirming that risk perception can be affected by negative international events. Research Limitations/Implications: A proper interpretation of the cognitive map requires an appreciation of the plasticity of risk, as perceptions are changed by scientific knowledge, media coverage and globalization of risk issues. Future research should examine these changes and determine other possible variables influencing risk ratings at different times. While stratified sampling helped the representativeness of the sample, convenience sampling was applied within each stratum. Future research might use probability sampling instead. This study gives risk analysts and policymakers a basis for understanding and anticipating public responses to risks and improving the communication of risk information among laypeople and decision-makers. Originality: This study is one of few to develop a cognitive map and investigate factors influencing risk perception in Jordan, in the volatile Middle East. Rather than surveying mainly students, it used a representative sample from Jordan’s 12 provinces.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".