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Record W1964375767 · doi:10.1177/147059580331006

`To Adapt or Not to Adapt'

2003· article· en· W1964375767 on OpenAlexaff
Shaista E. Khilji

Bibliographic record

VenueInternational Journal of Cross Cultural Management · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsCarleton University
Fundersnot available
KeywordsAdaptation (eye)Organizational cultureBusinessPublic relationsTest (biology)Cultural sensitivityMultinational corporationPolitical sciencePsychology

Abstract

fetched live from OpenAlex

This study draws out evidence from 11 organizations, local as well as multinationals, in Pakistan to test the `culture-sensitive' view. A number of conclusions are drawn. First, the findings support this view; evidence shows enough similarities exist between local organizations and multinationals to indicate that the influences of the parent companies of multinationals are weakened by the national characteristics of the environments in which they operate. Second, it is pointed out that although some policies of multinationals may be the same as those found in their parent companies, the practices certainly are not, because of adaptation to local norms. It is therefore suggested that a distinction between police) and practice be made in an organizational analysis of this kind. Third, despite culture-sensitivity of HRIV practices, their impact on employees is similar to what has been previously documented by researchers in the UK and the USA. It is left to further studies to debate whether HRM outcomes can be termed as universal.

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)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.004
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.077
GPT teacher head0.440
Teacher spread0.363 · 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 designQualitative
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

Citations94
Published2003
Admission routes1
Has abstractyes

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