When the going gets tough, do the tough go shopping?
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 study examines the impacts of consumer confidence on stockpiling behavior and, subsequently, retail inventory management. We show how stockpiling behavior evolved during the “Great Recession” of 2008–2009 as consumer confidence waned and demonstrate the impact of this development on inventory management. Drawing on the two‐segment household inventory theory consisting of nonstockpiling and stockpiling segments, we use a panel dataset (2005–2015) to calibrate household inventory holdings. This dataset then serves as input for a retailer‐level case study. Our empirical analysis reveals significant impacts from changing stockpiling behavior. When consumer confidence is low, both stockpiling and nonstockpiling segments respond by reducing weekly consumption rates; however, the stockpiling segment also significantly lengthens the time between shopping trips, and ultimately increases the duration of inventory holdings. These changes to consumption and stockpiling add complexity to inventory planning, requiring retailers to carefully adjust inventory levels to maintain service levels.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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