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Record W2514433424 · doi:10.5539/jms.v6n3p44

Sustainability and Corporate Governance: Theoretical Development and Perspectives

2016· article· en· W2514433424 on OpenAlexvenueno aff
Miguel Ángel Jaimes-Valdez, Carlos Armando Jacobo Hernández

Bibliographic record

VenueJournal of Management and Sustainability · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceSustainabilityBusinessCorporate social responsibilityCorporate sustainabilitySustainable developmentQuality (philosophy)Value (mathematics)Field (mathematics)Process managementAccountingPublic relationsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Sustainability in the management field has several benefits including improvements in food quality competitiveness, responsibility and trust. Additionally, has been related not only to improving the image of organizations but also to increasing their value. However, this concept is multifaceted and diverse, and sometimes an incongruity appears when business leaders just want to increase their sales. Consequently, corporate governance has emerged as a topic connected to the establishment of agreements and the implementation of improvements in three dimensions: environmental, social and economic. This essay intends to explore the benefits, challenges and opportunities of sustainability and corporate governance to demonstrate the desirability of incorporating these topics into organizational management. Additionally, different models of sustainable governance are described to identify their elements and the similarities among them. We conclude that collaboration is key in all models and that it is necessary to broaden theoretical development to enable the implementation of best practices of corporate governance and to ensure sustainability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.022
Scholarly communication0.0090.008
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2016
Admission routes1
Has abstractyes

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