Media Sosial Komunitas untuk Meningkatkan Eksistensi Komunitas dalam Wacana Politik Pemilu Presiden 2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it