Ethnic-sensitive governance: Redefining good governance in an ethnically polarized region
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: East Kalimantan has experienced ethnic conflicts and riots in the past. With Indonesia's capital relocating there, concerns about potential ethnic strife are rising. This research seeks to identify a model of good governance suited for ethnically polarized regions to prevent conflicts from escalating. The study focuses on the newly established Nusantara Capital City and its surrounding area. The objectives of this study are threefold: first, to examine whether areas with high ethnic polarization quantitatively show signs of ethnic antagonism or tensions qualitatively; second, to understand the existing mechanisms of ethnic solidarity and mobilization; and third, to identify the key elements that inhibit the transformation of potential conflict into actual conflict from both ethnic and governance perspectives. The significance of this study lies in its efforts to develop an effective model of good governance tailored for regions with high ethnic polarization.METHODS: Qualitative and analytical-exploratory methods were utilized to analyze both qualitative and quantitative data, with descriptive statistics enhancing the latter's analysis. Nusantara Capital City comprises 54 villages, housing a total population of 201,259, including 1,777 individuals in village bureaucracies. For qualitative data collection, 29 villages were purposively selected based on ethnic polarization, and respondents were chosen through purposive sampling. Additionally, data on the ethnic backgrounds of the 1,777 individuals in the village bureaucracies were gathered from village offices and key informants, resulting in 189 respondents and key informants for this study.FINDINGS: The results show that all village samples in Nusantara (100%) implement ethnic-sensitive practices in public service delivery. While various ethnic groups are present at management, staff levels, and in community organizations closely connected to village bureaucracies, the village bureaucracy predominantly comprises three major ethnic groups: Javanese, Banjarese, and Buginese, with average representations of 40.01%, 21.23%, and 21.03%, respectively. Among the local minority ethnic groups, Kutai, Paser, and Dayak have average proportions of 4.55%, 3.14%, and 1.84%, respectively. Despite the insufficient focus on the inclusion of vulnerable Indigenous Baliq people in governance, these practices have helped prevent ethnic polarization from escalating into conflict. Improving governance to be more ethnically sensitive could better address the needs of indigenous communities.CONCLUSION: The findings provide enhanced strategies for policymakers to implement effective governance in ethnically polarized areas or multi-ethnic regions by taking ethnic-sensitive governance into account. This can be accomplished by adopting ethically sensitive governance within government policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".