Travel risks in the COVID-19 age: using Zaltman Metaphor Elicitation Technique (ZMET)
Bibliographic record
Abstract
While the COVID-19 pandemic changed our economies, work habits and daily routines in significant ways, it also fundamentally impacted our travel behaviour. This study identifies travel risk factors when planning trips amidst the COVID-19 pandemic. Instead of using verbal-centric interviews, this study used image-based interviews, based on the Zaltman Metaphor Elicitation Technique (ZMET), to better understand travellers’ thoughts and feelings as the COVID-19 pandemic was an unprecedented experience for people living in the twenty-first century. The finding of the study identifies 15 specific travel risk factors and categorizes them into three deep metaphors (Uncertainty, Distrust, Pandemic New Normal). This study contributes to the current field of travel risk research, particularly in pandemic crises, providing specific reasons why people were afraid and/or hesitated to travel. Based on an intensive data analysis, this study discusses theoretical and operational implications that could be used to deliver more transparent, direct and effective communications to consumers.
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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 | 0.005 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".