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Record W4361801349 · doi:10.55365/1923.x2023.21.2

Reconceptualisation of Corporate Social Responsibility Model in the Era of Sustainable Development

2023· article· en· W4361801349 on OpenAlexvenueno aff
Monica Putri, Yuris Naili

Bibliographic record

VenueReview of Economics and Finance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability, Governance, and Employment Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilitySocial responsibilitySustainable developmentGovernment (linguistics)SanctionsPublic relationsEconomic JusticeSociologyBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

Corporate Social Responsibility must be based on the principle of social justice so that the implementation of Corporate Social Responsibility will not burden one party resulting in the ineffective implementation of Corporate Social Responsibility.This research aims to reconceptualize the model of Corporate Social Responsibility in the legal framework in the era of sustainable development to realize social justice.This research is legal research with the type of empirical legal research.Implementing Corporate Social Responsibility does not mean that the state delegates its overall responsibility to business actors but invites business actors to work together to create sustainable development and improve people's quality of life.Social problems can only be solved through social engineering because the causes and consequences are multidimensional and involve many people.This reconceptualization includes Reconceptualisation of the Idea of Implementing Corporate Social Responsibility, which is carried out with the application of the Social Capital Concept; Reconceptualisation of Funding; Reconceptualisation of the Duties of the Central Government and Regional Governments; Reconceptualisation of the Corporate Social Responsibility Forum; Reconceptualisation of Awards; and Reconceptualisation of Administrative Sanctions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0000.000
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 head0.079
GPT teacher head0.314
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations2
Published2023
Admission routes1
Has abstractyes

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