Decentralization as a Tool for Ethnic Diversity Accommodation: A Conceptual Analysis
Bibliographic record
Abstract
With the increase of ethnic conflicts and ethnic groups’ mobilizations for ethno-nationalism to secure and share state power, the concept of decentralization has also been getting attention in ethnically plural countries, and many of them have taken advantages of adopting decentralization policies for the empowerment of diverse groups in their state cum nation-building process. Similarly, the requests and supports for the adaptation of different forms of decentralization as to accommodate number of political and administrative demands and claims emerging from different ethnic groups within a country has also increased in the recent past. This has induced the researcher and international actors to develop different definitions, interpretations, and objectives for decentralization on its ethnic diversity accommodation perspective. This paper attempted to conceptualize the decentralization as a tool for ethnic diversity accommodation through reviewing the existing literary definitions, explanations and with researchers’ interpretive arguments. The finding reveals that decentralization initiatives, like other impacts, in number of ways, contributes to accommodate rights, interests, needs and claims of competing ethnic groups, especially of ethnic minorities and accommodate them within the larger political system and their local attachments. However, the success of this process highly depends on the mechanisms adopted for sharing powers and responsibilities; the nature, subject and decree of decentralized power; and the willingness of authorities to allow the groups to exercise those powers, with other factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".