Bibliographic record
Abstract
North Sumatra is a richly multi-ethnic province is made up of dozens of ethnic groups, both indigenous to the province and migrants or their descendants.Overall, groups indigenous to the province make up about 36 percent; the rest are migrants where the majority of them are Javanese.Although the city famous pluralism and openness, as well as money politics is rampant, primordialism sometimes a factor in local politics.In the mayoral election in 2010 in the city of Medan, for example, although most of the city's Muslims recently that Rahudman Harahap background questionable when it comes to honesty, there is a campaign that is very powerful and effective way to mobilize their support behind him, given that his rival in the second round mayor race is Sofyan Tan, an ethnic Chinese and Buddhist.Ethnic Chinese and, to a lesser extent, non-Muslims also rallied behind Tan, though not so openly (Aspinall, Warburton and Dettman 2011).As stressed throughout this paper, the choice of a system of Proportional Representation (PR) lists open intensive level of competition between the candidates, especially between candidates of the same party.One interesting result, in the field as in many other parts of the country, is the high level of turnover positions: for DPRD Medan for example, only 30 per cent of the successful candidates were established.Patronage is important, though it comes in many forms -not just the distribution of individual gifts and cash, but also long-term social assistance program that the candidate has in some cases been providing for years.As a result, it tends to only the wealthiest candidates -those who have significant personal assets they have, or can borrow or take donations from relatives or rich sponsorswhich has a strong chance of victory.Only a few candidates entered the political rivalry with clear ideas on development policies or government programs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".