Bibliographic record
Abstract
Ethnic conflict is shaped and mediated by the institutional context in which it occurs. Political institutions have a direct impact on the development of ethnic identity, its use in political mobilization, as well as the means available to negotiate group claims. They define citizens and non-citizens, majorities and minorities, and the allocation of political and economic resources. They define or deny group rights while also delimiting the means available to advance group interests. Ethnic identities are malleable, multiple, and not always politicized. Identities defined in racial, religious, and cultural terms may be fixed over long periods of time but they can also change. Voluntary or forced conversion, conquest and redefinition of political boundaries, or colonial policies are all examples of events that can reshape them. Furthermore, any single individual possesses multiple, overlapping ethnic identities as a member of a religious, cultural, or regional group. Which identity becomes a stronger source of group differentiation may vary from one set of circumstances to another. Even more so, these identities do not necessarily become sources of competition for resources, access to the state, or conflict. While group differentiation may be prevalent, it may not have an impact on the structure of socio-political organizations or the character of political mobilization. Political institutions are part of the context that shapes ethnic identity and mediates conflict. Differences in electoral systems might provide varied incentives to mobilize ethnic identity, sometimes even contributing to the formation of ethnic political parties.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".