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Record W104397840

The Trouble with Being Average

2009· article· en· W104397840 on OpenAlexaff
Cyril Bouquet, Andrew Crane, Yuval Deutsch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsYork University
Fundersnot available
KeywordsCorporate social responsibilityMultinational corporationBusinessReputationSocial responsibilityLegitimacyValue (mathematics)AccountingMarketingPublic relationsFinancePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Many researchers and senior executives now agree that under the right conditions, corporate social responsibility initiatives can simultaneously create value for society while also producing valuable rewards for companies. But can multinational corporations capitalize on their corporate social responsibility investments when they expand overseas? Do multinationals profit in foreign markets by investing in corporate social responsibility? Or are social initiatives simply a costly distraction for companies seeking to generate returns from their international ventures? On the one hand, investments in corporate social responsibility programs can enhance the legitimacy of a company operating outside of its domestic market by establishing a strong reputation for good citizenship. This reputation can smooth the path of international expansion and provide a solid foundation for international success. On the other hand, a commitment to corporate social responsibility can inhibit the ability of companies to reap cost advantages that may be available overseas.

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)0.007
metaresearch head score (Gemma)0.021
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.016
Scholarly communication0.0080.011
Open science0.0010.006
Research integrity0.0030.010
Insufficient payload (model declined to judge)0.0200.009

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 head0.008
GPT teacher head0.197
Teacher spread0.190 · 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
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

Citations0
Published2009
Admission routes1
Has abstractyes

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