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Record W3182236247 · doi:10.3390/businesses1020006

Supply Chain Responsiveness to a (Post)-Pandemic Grocery and Food Service E-Commerce Economy: An Exploratory Canadian Case Study

2021· article· en· W3182236247 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBusinesses · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBusinessPurchasingMarketingSupply chainProduct (mathematics)Exploratory researchSustainabilityService (business)PandemicPreparednessE-commerceCoronavirus disease 2019 (COVID-19)Economics

Abstract

fetched live from OpenAlex

The focus of this study looks at the motivations and rationale from a national survey of over 7200 Canadians in November 2020 into why they use online services to purchase food. As a result of the global COVID-19 pandemic, food supply chains have been significantly altered. Consumers are purchasing foods with different dynamics, including when they buy in-person at groceries, at restaurants or at food service establishments. Elements of the food supply chain will be permanently altered post-pandemic. The study looks at a specific set of factors, captured in the survey, namely, consumer price sensitivity to the costs of online food purchasing, growing sustainability-related concerns over food packaging and waste, and product sensory experience related to how online purchasing changes from in-person food selection. The end goal, emerging from a case study, is insight into the strategies and preparedness with which CPGs, food services, and retailers can better manage the supply chain in their food product offerings in the post-pandemic era.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.248
Teacher spread0.211 · 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