Understanding consumer stockpiling: Insights provided during the <scp>COVID</scp>‐19 pandemic
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
Abstract This article examines data collected early in the COVID‐19 pandemic to uncover the underlying factors that are related to consumer stockpiling in response to a global pandemic. A survey of 1325 American consumers from March 27, 2020 to March 29, 2020 revealed that 55.5% of respondents stockpiled. Locus of control (LOC), the extent to which a person believes the environment is controllable and responsive, is associated with the stockpiling decision. More specifically, after controlling for demographic characteristics, consumers with internal LOC are less likely to stockpiling than those with external LOC. We also find that consumers with higher health risk are more likely to stockpile. Together, our results provide valuable insight for practitioners and policy makers who are concerned with understanding and reducing consumer stockpiling during health‐related crises.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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