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Record W2106452593 · doi:10.1177/1350508411398728

A critical analysis of North American business leaders’ neocolonial discourse: global fears and local consequences

2011· article· en· W2106452593 on OpenAlexaff
Steve McKenna

Bibliographic record

VenueOrganization · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsYork University
Fundersnot available
KeywordsHegemonyOrientalismChinaColonialismContext (archaeology)SociologyCritical discourse analysisPower (physics)Discourse analysisAmbivalencePolitical economyPolitical scienceLawPoliticsIdeology

Abstract

fetched live from OpenAlex

Using a postcolonial analytic frame and critique this article investigates the nature of the discourse used by 24 North American business leaders to describe, understand and make sense of the economic development of China and India and contemporary international encounters. In particular the article investigates how business leaders discursively characterize this ‘threat’, how they (re)present China and India and, how they discursively construct the requirements of a response to this ‘threat’. An analysis of the interviews indicates the persistence of the discourse of (neo)colonialism (Orientalism) in the construction of the Other within the context of a view of China and India as developing and progressing towards a North American ideal. Despite this, North American business leaders also show ambivalence and uncertainty towards China and India. On the one hand they laud their success while damning them for their apparently exploitative social, economic and workplace systems and practices. Moreover, while they promote a Western development discourse concerning China and India, North American business leaders recognize that China and India are becoming centres of global economic power that are increasingly challenging the global hegemony of the United States. The article ends with a conclusion on the contribution of the article and in particular points to the value of Bhabha’s notion of the in-between’ spaces as a way forward for understanding developments in the global business environment.

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)0.012
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0240.037
Scholarly communication0.0120.006
Open science0.0010.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.237
Teacher spread0.211 · 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

Citations52
Published2011
Admission routes1
Has abstractyes

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