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Record W4241169930 · doi:10.1057/978-1-137-00772-8_749

Geocentric Staffing

2018· book-chapter· en· W4241169930 on OpenAlexaff
Leighton Wilks, Alain Verbeke

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultinational corporationStaffingEthnocentrismBusinessSubsidiaryHuman resource managementHuman resourcesValue (mathematics)Political scienceLaw

Abstract

fetched live from OpenAlex

Multinational corporations (MNCs) can staff their subsidiaries with parent country nationals (employees from the home country), host country nationals (employees from the subsidiary location), third country nationals (employees from a country other than the home or host country) or some mix thereof. The debate regarding the value of each of these groups originated with Perlmutter, who suggested that an MNC can hold an ethnocentric, polycentric or geocentric managerial orientation. This framework has become a guiding model in the field of global staffing. Here we trace the evolution of the debate, beginning with Perlmutter, moving on to strategic international human resource management and the tension between global integration and local responsiveness, and finally to current issues regarding knowledge creation and transfer. We conclude with a discussion of the implications of MNCs’ choice of managerial orientation on global staffing.

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 score0.009
Threshold uncertainty score0.029

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 studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.293
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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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