Bibliographic record
Abstract
The dissertation aims to contribute to the explanation of internal inter-group conflict, more narrowly of the conflict between majority and minority communal groups. It develops arguments that suggest the importance of inter-group economic inequality in bringing about inter-group hostility, and works toward providing empirical support for this causal connection by primarily relying on a large-N cross-national research design. This design culminates in multivariate regression models. Because of data availability issues, the task of addressing multiple potential determinants of the inter-group conflict advocated in the literature has been implemented by involving three datasets, of which two serve group-level analyses and one confines itself to the country level. The datasets are compilations of previous scholarly work, mainly based on the Ethnic Power Relations, Minorities at Risk (MAR), and Quality of Government data, with the addition of some new measurements, such as the main explanatory variable, economic inequality. Findings from all three datasets support the impact of horizontal economic inequality on inter-group hostility, measured either as group grievance or violent conflict. This double measurement of the inter-group conflict, as grievance and as violence, answers an intuition that not all low-to-medium strength hostility is doomed to develop into violent conflict. In fortunate conditions, the issues can be solved, or compromises may be reached without turning to violence. A large number of variables in the regression models operationalize constellations that influence the evolution of conflicts toward either peaceful solutions or armed collision. In general, the models provide support for previous expectations promoted in the literature regarding the beneficial impact of democracy and political equality of the groups, but also for the adverse impact of the opportunities for insurrection. Some institutional variables have been defined in ways that they allow for distinguishing between the outcomes of two brands of policies recommended for heterogeneous societies, as advised by Lijphart and Horowitz. Further benefits from the project include the construction of an almost complete list of communal groups worldwide, with 860 groups, which usefully contextualizes MAR’s selection of 282 minority groups. Data also allowed for comparing the causes of communal and social 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".