Local Government Services and Social Margins: The Case of Plantation Community in Sri Lanka
Bibliographic record
Abstract
Decentralization and local governance have been perceived as an effective tool for efficient, accountable, responsive and impartial public service delivery to all segments of citizens irrespective of ethnicity, race, gender, caste, language, social groupings etc. This paper, thus, throws more light on local government service delivery in the minority regions, especially looking at the status of plantation community in the local governance structures in Sri Lanka. The study finds that although Sri Lanka has adopted decentralized local government system at different levels, it has often been failed to effectively accommodate and address interests of ethno-linguistic minorities –Plantation Tamils. Exclusion of the plantation community in the service delivery of local government authorities has been a significant flaw of local government system which fundamentally challenges the notion of inclusive state, quality of government and democracy. The study particularly explores major factors that preclude plantation community from enjoying local government services. This issue, thus, stems a critical question about their status of citizenship rights and quality of governance in Sri Lanka. This study also may be a reflection of the plight of minorities in other multi-ethnic nations where discriminatory laws and policies affect right to access local governance and democracy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".