Representation of ethnic minorities in international relations as a problem of inclusive diplomacy in the 21st century
Bibliographic record
Abstract
The aim of the study was to establish the role of ethnic minorities in the United States, Canada, Ukraine and Australia in the process of decision-making at the state level. The methods used included political and sociological analysis, historical comparative analysis, and statistical analysis. A historical analysis of the cultural diplomacy of the Crimean Tatars during the establishment of relations with Turkey revealed the importance of the ethnic factor in building diplomatic ties, in particular through a shared historical heritage, migration processes, and Turkey's current support for Ukraine's territorial integrity and the rights of the Crimean Tatar people in the international arena. Examples of multi-channel diplomacy by the United States and Canada demonstrated the importance of an institutionalised approach to involving ethnic minorities in the diplomatic service, which contributes to both internal social stability and the formation of an effective and culturally sensitive foreign policy capable of strengthening international ties in the face of global challenges. It was found that in the United States of America during the 20th century, the diplomatic service was predominantly composed of white Americans. As of 2024, only 27 diplomatic service employees with a political specialisation were Asian, Haitian, or indigenous, with another 10 marked as “multi-race” and 4 of unspecified origin, accounting for 6% of the country's population. In Canada, the government's policy of involving ethnic minorities in diplomacy has contributed to an increase in their share from 16.6% in 2017 to 23.7% in 2023. The systematic integration of ethnic minorities into decision-making and international affairs through diplomacy requires structural reforms and financial incentives. To ensure equal representation of ethnic minorities in diplomacy, it is necessary to implement comprehensive policies to overcome structural barriers, support professional development, and create transparent mechanisms for access to diplomatic careers, including international initiatives, national human resources strategies, scholarship and mentoring programmes, and formal recognition of the role of indigenous peoples in foreign policy processes. The results of this study can be applied in the development of inclusive diplomacy policies at the national level
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".