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Record W3041468641 · doi:10.3390/su12145544

Scoping the Evolution of Corporate Social Responsibility (CSR) Research in the Sustainable Development Goals (SDGs) Era

2020· article· en· W3041468641 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.

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCorporate social responsibilitySustainable developmentDescriptive statisticsInterdependenceSustainabilityBusinessTransparency (behavior)Sustainability reportingThematic analysisPublic relationsPolitical scienceSociologyQualitative researchSocial science

Abstract

fetched live from OpenAlex

Amidst a contemporary culture of climate awareness, unprecedented levels of transparency and visibility are forcing industrial organizations to broaden their value chains and deepen the impacts of Corporate Social Responsibility (CSR) initiatives. While it may be common knowledge that the 2030 agenda cannot be achieved on a business-as-usual trajectory, this study seeks to determine to what ends the United Nations Sustainable Development Goals (SDGs) have impacted CSR research. Highlighting linkages and interdependencies between the SDGs and evolution of CSR practice, this paper analyzes a final sample of 56 relevant journal articles from the period 2015–2020. With the intent of bridging policy and practice, thematic coding analysis has supported the identification and interpretation of key emergent research themes. Using three descriptive categorical classifications (i.e., single-dimension, bi-combination of dimensions, sustainability dimension), the results of this paper provide an in-depth discussion into strategic community, company, consumer, investor, and employee foci. Furthermore, the analysis provides a timely and descriptive overview of how CSR research has approached the SDGs and which ones are being prioritized. By deepening the understanding of potential synergies between business strategy, global climate agendas and the common good, this paper contributes to an increased comprehension of how CSR and financial performance can be improved over the long-term.

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)0.033
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.103
GPT teacher head0.347
Teacher spread0.244 · 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