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Record W7143411255 · doi:10.5281/zenodo.19333550

GLOBAL DIALOGUES: INVESTIGATING THE OUTCOMES OF CROSS-CULTURAL EXCHANGE

2025· article· en· W7143411255 on OpenAlexaffabout
Daniel Christopher Dr. O'Connor

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsAppropriationCultural exchangeCultural diversityCultural competenceReciprocalBridging (networking)Bridge (graph theory)Cultural appropriation

Abstract

fetched live from OpenAlex

This study explores the influence of cultural exchange on global understanding and intercultural communication, emphasizing its role in fostering tolerance, empathy, and mutual respect among diverse communities. Focusing on Niger Delta diasporan residents in Saskatoon, Saskatchewan, Canada, the study adopts a comparative approach to examine how individuals bridge cultures through educational programs, artistic collaborations, and intercultural festivals. Employing literature review and archival analysis, the research investigates the ways cultural exchange cultivates inclusivity and harmonious interaction while identifying challenges such as cultural appropriation and misrepresentation. Findings highlight the importance of respectful and reciprocal engagement, advocating for practices that empower marginalized voices and promote authentic cultural understanding. The study concludes by emphasizing the vital role of sustained cultural exchange initiatives, such as artist residencies and cultural festivals, in dismantling stereotypes, bridging divides, and nurturing a globally interconnected community that values the richness of human diversity.

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)0.010
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0060.009
Scholarly communication0.0090.008
Open science0.0010.015
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.079
GPT teacher head0.358
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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 routes2
Has abstractyes

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