Mapping Ethnopolitical Conflictivity: Prognostic Potential and Methodological Limitations
Bibliographic record
Abstract
The article presents a critical analysis of scientific and practical results of using the mapping method and an analysis of the possibilities created by an analytical assessment model of ethnopolitical conflictivity potential in studying ethnopolitical conflicts in various countries and regions of the world. These approaches are an integral part of the identity research agenda promoted at IMEMO and implemented in the development of the Ethnopolitical Conflict Monitor, a map and a computer application for comparatively analyzing in dynamic and predicting areas of ethnopolitical conflictivity. The Monitor was put together as a result of the 2015-2017 Russian Science Foundation project ‘Regulating Interethnic Relations and Managing Ethnic and Social Conflicts in the Contemporary World: The Potential of Civiс Identity (Comparative Political Analysis)’ executed at IMEMO. Ethnopolitical Conflictivity Monitor as a science project involves a continuous monitoring of conflicts with an ethnic component, where identity serves as a non-material resource of each conflicting side and identity politics is a crucial tool in regulating such conflicts. This type of monitoring is performed with the help of scientists from many IMEMO structures, specialists on the analyzed countries and regions. The work on the Monitor involves an expert selection of conflicts in countries and regions of the world, typology of confrontations in accordance with the formulated classification, analysis and classifying of regulation practices, especially those with identity politics as a regulation tool, estimation of the practices in correlation with the current state of affairs and regulation prospects. Ethnopolitical Conflict Monitor has an evident practical significance. It can help in developing guidelines on interethnic tension mitigation and and promoting civic solidarity important for Russia's social and political development.
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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 | 0.111 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".