Travel risks in the COVID-19 age: using Zaltman Metaphor Elicitation Technique (ZMET)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it