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Record W4390095522 · doi:10.1002/9781118469392.ch13

Corporate Social Responsibility and Psychologically Healthy Workplaces

2014· other· en· W4390095522 on OpenAlexaff
Jennifer L. Robertson, Julian Barling

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsCorporate social responsibilityPublic relationsOrganizational commitmentSocial responsibilityBusinessBusiness ethicsJob satisfactionPsychologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Corporations have increasingly been held accountable for their actions and the social and environmental consequences that emerge from them. Top business leaders around the world have implemented an array of ethical, social, and environmentally responsible practices and policies. These practices and policies have come to be known collectively as corporate social responsibility (CSR), and increasing numbers of organizations are committed to improving their organization's CSR performance. This chapter aims to synthesize research that has focused on individual-level outcomes of CSR. It provides a framework that integrates the research on the different aspects of CSR and psychologically healthy workplaces. Several studies have established a positive link between CSR and employees' organizational commitment. The chapter also provides examples of organizational best practices with respect to employee involvement in CSR. It explores the positive influence organizational ethics can have on both employees' organizational commitment and job satisfaction.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.055
GPT teacher head0.298
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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