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Record W4413384586 · doi:10.54097/z12aav41

The Three Waves of Globalization: Evolution, Impacts, and Future Challenges

2025· article· en· W4413384586 on OpenAlexaff
Guanxiang Huang

Bibliographic record

VenueHighlights in Business Economics and Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsGlobalizationEconomic geographyBusinessEconomicsMarket economy

Abstract

fetched live from OpenAlex

Globalization has significantly shaped economic, political, and social structures over the past 150 years, evolving through three waves. This study examines the key drivers, major impacts, and comparative differences among these waves. The first wave (1870–1914), driven by industrialization, technological advancements, and trade liberalization, expanded global markets but reinforced colonial inequalities. The second wave (1944–1971), shaped by post-war reconstruction and the Bretton Woods System, promoted economic growth but was constrained by Cold War divisions. The third wave (1989–present) has been fueled by digital transformation, financial deregulation, and the rise of emerging economies, enhancing connectivity but also contributing to economic disparities, labor displacement, and environmental concerns. Comparative analysis highlights legal developments, gender inclusion, and corporate social responsibility (CSR), revealing progress in regulatory structures and workplace equality, though challenges remain. The study also explores current globalization challenges, including job insecurity, financial instability, and geopolitical tensions, proposing intergovernmental cooperation, policy reforms, workforce reskilling, and sustainability strategies as potential solutions. The findings suggest that balancing economic integration with social and environmental responsibility is crucial for ensuring equitable and sustainable globalization in the future.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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