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Record W4220756411 · doi:10.1080/09593969.2022.2056906

Stepping up as an essential service: grocery retailing and the COVID-19 pandemic in Canada

2022· article· en· W4220756411 on OpenAlexaffabout
Jenna Jacobson, Frances Gunn, Tony Hernández

Bibliographic record

VenueThe International Review of Retail Distribution and Consumer Research · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsThematic analysisBusinessGovernment (linguistics)PandemicResilience (materials science)Public relationsCoronavirus disease 2019 (COVID-19)Work (physics)Service (business)Grocery storeMarketingPsychological resilienceQualitative researchPolitical scienceSociologyPsychologyEngineeringMedicine

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has elevated the ‘essential service’ question to the forefront of government policy, business functioning, and public discourse. This qualitative study uses community disaster resilience and institutional work theory to analyse the responses of Canadian grocery retailers to COVID-19. Based on a thematic analysis of 53 grocery retailers’ website messaging over three periods at the height of the first wave of the COVID-19 pandemic in Canada, the research identifies ten themes that capture the retailers’ response to the pandemic. Focusing on major grocery retailers in Ontario, the research tracks messaging concerning the elevated community role of grocery retailers through a period of crisis. We develop a conceptual framework to understand the community disaster resilience levers of grocery retailing during a pandemic. The research highlights the shift in the balance of messaging concerning the institutional logic of grocery retailers, away from market forces towards a community logic. The findings illustrate how grocery retailers stepped up as an essential service and extended their reach beyond the bounds of their underlying institutional logic to encompass the public good.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.132
GPT teacher head0.374
Teacher spread0.242 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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Same venueThe International Review of Retail Distribution and Consumer ResearchSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207