Intercultural Relations in Georgia and Tajikistan: A Post-Conflict Model
Bibliographic record
Abstract
The paper presents the results of two studies of intercultural relations in post-Soviet Georgia and Tajikistan. These countries have in common a sharp decline in cultural diversity as a result of wars and conflicts, and this model of intercultural relations on post-Soviet space was identified as a post-conflict model. The goal of this study was to evaluate three hypotheses of intercultural relations: multiculturalism, contact and integration (Berry, 2017) among majority members and the ethnic Russian minorities. We surveyed 312 Ethnic Russians and 298 Georgians in Georgia; 277 Ethnic Russians and 317 Tajiks in Tajikistan. The studies used scales from the MIRIPS questionnaire. To test the three hypotheses of intercultural relations we followed a Structural Equation Modeling (SEM) approach. The multiculturalism hypothesis found partial support in all four groups in Georgia and Tajikistan. The contact hypothesis received partial support in Tajiks and in Ethnic Russians in Georgia and was not supported among Ethnic Russians in Tajikistan and Georgians. The integration hypothesis was fully supported in Tajiks and Ethnic Russians in Georgia, partially supported among Ethnic Russians in Tajikistan and was not supported among Georgians. The results obtained in these two countries are discussed taking into consideration the sociocultural contexts and recent history of wars and conflicts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".