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Record W3215934206 · doi:10.4337/9781788976817.00010

Internationalization of location-bound resources: cases from Canada

2021· book-chapter· en· W3215934206 on OpenAlexaboutno aff
Hamid Etemad, Hamed Motaghi

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2021
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationPoint (geometry)Economic geographyBusinessLongitudinal studyPolitical sciencePublic relationsMarketingRegional scienceSociologyGeographyInternational tradeStatistics

Abstract

fetched live from OpenAlex

This chapter addresses a gap in the internationalization theory. It compares the internationalization of location-bound events and resources with the traditional theories of internationalization and points to their few similarities and mainly differences. Two in depth and longitudinal case-studies of highly popular and growing events in Montreal, Canada, prior to the emergence of COVID-19 crisis document significant differences between the traditional and location-bound internationalization, where international customers travel to Montreal to benefit from such events. These cases also point to the important impact of influential factors and evolving processes. They also uncover that communication and information technologies play critical roles in the internationalization outcomes over time. The longitudinal aspects of these case-studies also point to the indirect internationalization of complementary events and services over time due to their interactions with the other firms, and international customers at home during the internationalized local events. The chapter also discusses the managerial lessons, theoretical implications, and public policy recommendations, followed by a brief epilogue updating the status of the two cases at the end.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.203
Teacher spread0.183 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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