Hoarding essential products during the COVID-19 pandemic
Bibliographic record
Abstract
Research methodology This case was created based on secondary sources available in the public domain (i.e. news articles). This case has been taught in an undergraduate course of principles of management under the chapter on ethics. Case overview/synopsis When the COVID-19 pandemic broke out, people panicked and rushed to purchase essential products such as hand sanitizers, antibacterial soaps, disinfectant wipes and face masks. The images of a panicked public inspired the brothers Matt and Noah Colvin who amassed and hoarded stockpiles of these essential products to make immense profit. They claimed that their trade approach was legitimate. Yet by an ironic twist of fate, their unorthodox acts were revealed in the media and consequences came in threes: the public vilified the hoarders, the online marketplaces kicked them out and the authorities opened an investigation about alleged price-gouging practices. Complexity academic level This case study may be used in classroom discussions on the concepts of hoarding and price gouging in the following academic programs: bachelor’s in business administration, master of science in business administration and MBA programs. This case study may be used in the following academic courses: ethics in business, responsible management, fundamental of management and organizational behavior.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".