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Record W2151783496 · doi:10.1002/nvsm.386

Standardization or localization? Executing corporate philanthropy in international firms

2009· article· en· W2151783496 on OpenAlexaff
Michael A. Merz, John Peloza, Qimei Chen

Bibliographic record

VenueInternational Journal of Nonprofit and Voluntary Sector Marketing · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCorporate social responsibilityReputationStandardizationBusinessInvestment (military)AccountingPoint (geometry)Financial marketIndustrial organizationFinancePublic relations

Abstract

fetched live from OpenAlex

Abstract Corporate social responsibility (CSR) has become a key component of a firm's reputation. The reputational vulnerabilities and pressure for CSR are perhaps greatest among international firms with business activities across many countries and cultures. Although the strategies of firms entering new markets have been well researched, the CSR component of the market entry decision has been largely ignored, despite its significant relationship with the financial performance of the firm. Further, previous research has largely considered CSR from an environmental performance point of view, and thus has focused on a minimum level of investment in CSR as opposed to the optimal form of the investment. Our paper seeks to address this gap by examining market entry decisions as they relate to corporate philanthropy. Copyright © 2009 John Wiley & Sons, Ltd.

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)0.012
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.009
Scholarly communication0.0100.009
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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

Citations13
Published2009
Admission routes1
Has abstractyes

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