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Record W2888221951 · doi:10.5539/jms.v8n3p1

A Multilevel Model of Responsibility Towards Employees as a Dimension of Corporate Social Responsibility

2018· article· en· W2888221951 on OpenAlexvenueno aff
Aviad Bar-Haim, Orr Karassin

Bibliographic record

VenueJournal of Management and Sustainability · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityIndustrial relationsContext (archaeology)Human resource managementDimension (graph theory)BusinessMultilevel modelPower (physics)Social responsibilityPredictive powerPublic relationsKnowledge managementPolitical scienceManagementEconomicsComputer science

Abstract

fetched live from OpenAlex

This multilevel study addresses labor relations (LA) and human resource management (HRM) practices within the context of corporate social responsibility (CSR). The study adds to the growing literature on multilevel CSR by addressing the specific aspect of responsibility towards employees through LA-HRM practices in industrial firms. We design a multidimensional model of LA-HRM oriented CSR with the wider institutional environment, industrial setting and organizational setting as antecedents. The model and findings allow for a broad view of factors associated with practices of LA-HRM as important attributes of CSR. The predictive power of the institutional setting as well as industrial setting are shown to be moderately strong, while contrary the research hypothesis the organizational setting generally exhibits weak predictive power. The former finding reinforces the central role of the external environment and actors in firms’ internal application of LA-HRM practices and CSR. The later finding suggests that contrary to previous assertions, LA-HRM is generally not within the discretional power and influence of firms, and not a not a key area in the context of firms’ voluntary CSR policy but is dominated by externally mandated regulatory requirements.

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)0.011
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.044
GPT teacher head0.302
Teacher spread0.258 · 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.

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

Citations11
Published2018
Admission routes1
Has abstractyes

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