Media Sosial Komunitas untuk Meningkatkan Eksistensi Komunitas dalam Wacana Politik Pemilu Presiden 2019
Bibliographic record
Abstract
This research discusses NET.Good People community in Jakarta, Bogor, Depok, Tangerang and Bekasi (Jabodetabek) as part of efforts to strengthen their existence in political discourse during the 2019 Presidential Election. The instagram-based NET.Good People community in Jabodetabek came up with a political discourse content to maintain the image of NET.TV in the community without taking side with one of the presidential candidate pairs but rather asking the public not to abstain from voting. This research uses qualitative approaches and Critical Discourse Analysis method with the variants of Norman Fairclough. The results of this research show that the NET.Good People community in Jabodetabek did not take side with one of the presidential candidate pairs in the 2019 election. However, this research highlighted the importance of community members to take part in politics without being an abstainer in the presidential election in line with the messages they have sent on the instragram. The instagram messages were neutral and substantively called on the public to vote in the 2019 presidential election for the sake of a better Indonesia in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".