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Record W3022937547 · doi:10.1002/tie.22139

<scp>Sport and International Management</scp>: Exploring research synergy

2020· article· en· W3022937547 on OpenAlexaff
Mike Szymanski, Richard Wolfe, Wade Danis, Fiona Lee, Marilyn A. Uy

Bibliographic record

VenueThunderbird International Business Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMultinational corporationContext (archaeology)Focus (optics)BusinessInternational businessIdentity (music)Knowledge managementPublic relationsMarketingPsychologyPolitical scienceManagementComputer scienceEconomics

Abstract

fetched live from OpenAlex

Abstract The objective of this article is to indicate how international business (IB) research can benefit from using sport as a research context. We present the rationale for studying organizational phenomena within sport, with a focus on benefits specific to IB research, and present examples wherein sport is used to study organizational phenomena relevant to IB. Among the examples we present, we focus on the influence of identity integration on performance of multinational teams, the influence of international experience on performance of multinational teams, and the influence of cultural differences on emotions in organizations. We conclude with suggestions for future research.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.239
GPT teacher head0.400
Teacher spread0.162 · 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 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

Citations13
Published2020
Admission routes1
Has abstractyes

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