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Record W4295345211 · doi:10.1108/tcj-08-2021-0118

Hoarding essential products during the COVID-19 pandemic

2022· article· en· W4295345211 on OpenAlexaff
Abderrahman Hassi, Giovanna Storti

Bibliographic record

VenueThe CASE Journal · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsEmployment and Social Development Canada
Fundersnot available
KeywordsHoarding (animal behavior)BachelorPublic relationsCoronavirus disease 2019 (COVID-19)PandemicSociologyMarketingPolitical scienceBusinessLaw

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.078
GPT teacher head0.286
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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