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Record W3109924491 · doi:10.5539/jms.v10n2p121

Sustainability Effort Acceleration as Measured by Increased Propensity of Corporate Social Responsibility

2020· article· en· W3109924491 on OpenAlexvenueno aff
Mark Reavis, Jack E. Tucci

Bibliographic record

VenueJournal of Management and Sustainability · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilitySustainabilityEthosSocial responsibilitySociologySocial sustainabilityPublic relationsEnvironmental ethicsNorm (philosophy)Political scienceLawEcology

Abstract

fetched live from OpenAlex

Corporate Social Responsibility (CSR) is the foundational bedrock for sustainability efforts. Corporate Social Responsibility is also becoming the norm rather than the exception due to social awareness created by curricula that highlights areas of both social and environmental inequality and has recently emerged as a bona fide strategic option globally. Howe and Straus predicted the growth of Corporate Social Responsibility in their seminal work, Millennials Rising. This paper extends and validates that earlier work through the illumination of recent causal factors and changes in society. The combination of proactive equality initiatives resulting in changes in leadership and value anchoring by college major illustrates that millennials’ ethos more strongly align with both social and environmental sustainability philosophies. The forthcoming millennial upheaval, as posited by Howe and Strauss, is evidenced by “strong belief statements” as interpreted by the raters in this study.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.037
GPT teacher head0.265
Teacher spread0.228 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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