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Record W3008363120 · doi:10.3390/jrfm13030041

Mainstreaming Global Sustainable Development Goals through the UN Global Compact: The Case of Visegrad Countries

2020· article· en· W3008363120 on OpenAlexvenueno aff
Štěpánka Zemanová, Radka Druláková

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentMainstreamingCorporate social responsibilitySustainabilityBusinessObligationReputationSustainability reportingPolitical sciencePublic relationsLaw

Abstract

fetched live from OpenAlex

Since 2016, the United Nations Global Compact (UNGC), one of the most prominent worldwide corporate social responsibility and sustainability initiatives, has been linked to the Sustainable Development Goals (SDGs). However, despite the enormous scholarly interest in the UNGC since the very beginning, its impact on the integration of the SDGs into the business activities, risk management and reporting of its participants remains understudied. This paper examines support and action for the SDGs among companies from the Visegrad Four (V4) countries. It attempts to find out whether the recent UNGC efforts result in their mobilisation towards the SDGs’ implementation or merely creates a new space for instrumental adoption to improve image and reputation. The paper adopts qualitative content analysis of 42 Communications of Progress (COPs), submitted by 25 companies from the V4 in 2017–2019. The related self-assessments in the UNGC Participation Database were also used. It reveals that the companies obviously fulfil their obligation to report their activities related to SDGs but fail to provide relevant details. Moreover, divergences between the challenges faced by V4 countries and the priorities of the companies related to individual SDGs are also identified. This raises serious concerns about the UNGC’s practical effects.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.238
Teacher spread0.226 · 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 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

Citations18
Published2020
Admission routes1
Has abstractyes

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