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Record W7125195470 · doi:10.65324/wewf007

Comparative characteristics and ways of doing business in Singapore and Canada

2025· article· W7125195470 on OpenAlexaboutno aff
Kristina D. Gvasaliya, Ksenia Kolesnikova

Bibliographic record

VenueWorld Economy and World Finance · 2025
Typearticle
Language
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipBusiness modelBusiness developmentBusiness environmentDigital economyInternational businessKey (lock)

Abstract

fetched live from OpenAlex

The purpose of this article is to identify the problems of institutional differences in the business environments of Canada and Singapore, as examples of Eastern and Western entrepreneurship models successfully integrated into the global economy yet demonstrating different development dynamics. The objectives of this paper are to highlight key features of the business environments of Singapore and Canada, and to analyze the specifics of business development in these countries, their infrastructure, and economic challenges. The research methodology utilized a comparative analysis, description, specification, inductive and deductive methods, and synthesis. The article analyzes macroeconomic indicators, international ease of doing business and global competitiveness rankings, regulatory frameworks, infrastructure, and cultural characteristics of these countries. The study results demonstrate that Singapore has a more favorable business environment due to its high level of economic openness, developed digital and logistics infrastructure, and focus on a “green economy”. Canada, on the other hand, has a higher level of innovative potential but faces challenges with outdated infrastructure and complex regulations. Based on the analysis, priority areas for business development in these countries have been identified.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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