MétaCan
Menu
Back to cohort
Record W2797119265 · doi:10.1002/csr.1500

How are supply chains addressing their social responsibility dilemmas? Review of the last decade and a half

2018· article· en· W2797119265 on OpenAlexaff
Mohamed Basta, James Lapalme, Marc Paquet, Patrick Saint‐Louis, Tarek Abu Zwaida

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsStakeholderCorporate social responsibilitySupply chainBusinessSocial responsibilityWork (physics)Set (abstract data type)Quality (philosophy)MarketingPublic relationsPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Supply chain social responsibility is increasingly a pressing concern to organizations of all sizes. Justified by its impact on the bottom line, various measures were adopted for resolution and prevention. However, despite the abundance of literature on the topic, there continues to be a lack of evidence on which of the measures are the most prolific, that is, how the concern of interest is actually handled. Such evidence would highlight missed opportunities and set the stage for future research. Toward this goal, the authors conducted a mapping study analyzing 590 articles. The findings reveal that corporate social responsibility, sustainable reporting, and social life cycle assessment are the most used methods whereas systems thinking ranks far behind. This work is original in that it is the first of its kind to reveal such findings scientifically. Practical implications of this work include reducing the supply chain's social footprint, ameliorating stakeholder quality of living, and mitigating social risk.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.035
GPT teacher head0.233
Teacher spread0.198 · 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 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

Citations20
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCorporate Social Responsibility and Environmental ManagementSame topicSustainable Supply Chain ManagementFrench-language works237,207