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Record W4390818452 · doi:10.3389/fnut.2023.1284377

The paradox of corporate sustainability: analyzing the moral landscape of Canadian grocers

2024· article· en· W4390818452 on OpenAlexafffundabout
Samantha Taylor, Sylvain Charlebois, Tammy Crowell, Bryce Cross

Bibliographic record

VenueFrontiers in Nutrition · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsSaint Mary's UniversityDalhousie University
FundersDalhousie University
KeywordsTransparency (behavior)SkepticismSustainabilityAccountabilityBusinessProfitability indexAccountingPerceptionEconomicsMarketingPublic economicsFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

Food prices have experienced unprecedented increases in recent times. Simultaneously, grocers are facing allegations of capitalizing on inflation to generate unjustifiable profits. Escalating expenses and a lack of transparency have engendered heightened consumer skepticism. This perceived presence of barriers and excessive profitability gives rise to ethical concerns. Our case study delves into the ethical landscape surrounding Canadian grocers, aiming to probe the public's demand for accountability. To comprehend the factors responsible for the transformation in consumer perception of Canadian grocers in 2022, we conducted an analysis utilizing data from consumers, corporate watchdogs, and industry sources. We extended the paradox perspective on corporate sustainability framework to include a historical aspect to use as our analytical lens. This study sheds light on the alterations in circumstances that have led Canadian consumers to question entire industries and accounting practices that were previously considered unproblematic. As a remedy, we recommend the establishment of a mandatory code of conduct for grocers and an enhancement in the transparency of financial reporting. Paradoxically, corporate profits may continue to grow when societal needs are no longer perceived as being neglected or, even worse, exploited.

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)0.001
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 score0.579
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2024
Admission routes3
Has abstractyes

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