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Record W2472043971 · doi:10.1108/cpoib-03-2016-0004

How might we study international business to account for marginalized subjects?

2016· article· en· W2472043971 on OpenAlexaff
Gabrielle Durepos, Ajnesh Prasad, Cristian E. Villanueva

Bibliographic record

VenueCritical Perspectives on International Business · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsScholarshipPositivismEmancipationOriginalitySociologyValue (mathematics)Field (mathematics)Critical theoryInternational businessSocial scienceEpistemologyPolitical sciencePoliticsLawQualitative research

Abstract

fetched live from OpenAlex

Purpose The aim of this article is to encourage critical scholars of international business (IB) to engage with scholarship that turns to practice and situates knowledges. The paper contends that such undertakings have the potential to constructively politicize research in the field of international business. Design/methodology/approach The paper discusses the need for future research in the field to be studied more critically so as to be able to focus attention on those subjects detrimentally impacted by the operation of IB. It further identifies possibilities for doing so. Findings The paper argues that turning to practice and situating knowledges represents a move towards the emancipation of subjects marginalized – and, all too often, silenced – in the ordinary functioning of IB. Originality/value Moving against the grain of positivist orientated approaches to research in the field, whilst simultaneously building on the critical traditions to the study of IB, we consider how future scholarship might account for marginalized subjects.

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.064
Scholarly communication0.0160.024
Open science0.0020.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.028
GPT teacher head0.298
Teacher spread0.270 · 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.

Study designTheoretical or conceptual
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

Citations17
Published2016
Admission routes1
Has abstractyes

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