China Town Magazine and Indonesian-Chinese Identity
Bibliographic record
Abstract
The new democratic political system in Indonesia recognizes Indonesian-Chinese as part of the national building. In the post-Suharto era, they are enjoying their cultural identity including freedom of press and freely to use their mother language. In fact, they were still develop their identity inside Indonesia as the multi-cultural country. The magazine called China Town is one of the Indonesian-Chinese Community Magazine. The magazine is not merely as the media which periodically reporting Indonesian-Chinese activities and opinions, but also as the representation of their existence and also identity. This article attempts to measure the role of the magazine particularly concerning on the identity issues. Specifically, this research will examine to what extent the China Town magazine achieve the objectives in terms of media coverage in order to develop and strengthen their identity? This is a qualitative study with content analysis. The empirical data found that, the China Town magazine have attempted tries to convince that Indonesian-Chinese is part of the Indonesian nation, as the Indonesian identity, and they are not exclusive as well as homogenous community. However, the magazine have also expressed and emphasized that Indonesian-Chinese were part of Chinese diaspora. It portrays that the magazine gave a balance information between Indonesian mainstream media and Chinese news.
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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 | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".