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Record W2112326585 · doi:10.1177/001979391306600304

U.S. Multinationals and the Control of Subsidiary Employment Policies

2013· article· en· W2112326585 on OpenAlexaboutno aff
Anthony Ferner, Jacques Bélanger, Olga Tregaskis, Michael Morley, Javier Quintanilla

Bibliographic record

VenueIndustrial and Labor Relations Review · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsSubsidiaryMultinational corporationOptimal distinctiveness theoryNationalityBusinessControl (management)Parent companyInternational tradePolitical scienceEconomic geographyDemographic economicsEconomicsManagementImmigration

Abstract

fetched live from OpenAlex

The authors examine whether U.S. multinational companies (MNCs) are distinctive in the degree to which they exert direct control over policy on human resources and employment relations (HR/ER) in their foreign subsidiaries. The results confirm the distinctiveness of U.S. MNCs in their greater degree of direct control of policy, compared not only with non-U.S. firms but with every other major nationality or national grouping of MNCs: France, Germany, the Nordic group, the rest of Europe, and Japan. U.S. control of HR/ER policy is greater not just in the aggregate, but for most individual items. Finally, while levels of control over subsidiaries vary among host countries studied (Canada, Ireland, Spain, and the United Kingdom) the greater U.S. orientation to control relative to non-U.S. MNCs holds regardless of host.

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)0.002
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.307
Teacher spread0.272 · 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

Citations35
Published2013
Admission routes1
Has abstractyes

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