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Record W7047048136

An "emerging challenge": The employment practices of a Brazilian multinational company in Canada

2013· article· en· W7047048136 on OpenAlexaboutno aff

Bibliographic record

VenueArrow@dit (Dublin Institute of Technology) · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationSubsidiaryContext (archaeology)Foreign direct investmentInternational businessSpace (punctuation)Emerging marketsKnowledge transfer
DOInot available

Abstract

fetched live from OpenAlex

Although the literature in international human resource management has developed greatly over recent years, our understanding of the dynamics of the transfer of HR practices in multinational companies (MNCs) from emerging economies with subsidiaries in advanced economies is found wanting. This study addresses this gap in our knowledge by investigating the transfer of employment policies of a Brazilian MNC to its Canadian subsidiaries. It examines interrelated questions about the influence of an emerging-economy parent-business system and how this interacts with the well-developed institutional regulation of the host country in a context of complex relations of dependence and dominance. Our prior expectation that the MNC would have had to adapt its policies to the ‘Canadian way’ was not borne out by the evidence. Instead the Brazilian MNC was found to be adept at capturing significant components of the host country’s institutional setting in a manner that gave it the space to determine the ‘rules’ for its own advantage. That it was able to do so was, in large part, shaped by the market context of the firm and by Canada’s dependence on foreign investment and, in turn, by the political relations of dependence that such reliance engendered. Broader lessons from the case analysis are offered.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.461
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.286
Teacher spread0.262 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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