The Audio Series Production Team's Strategy for "Catatan Pembalasan Fajar" In Retaining "Noice" Application Listeners
Why this work is in the frame
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Bibliographic record
Abstract
This research aims to determine the production team's strategies for maintaining listeners of the audio series "Catatan Pembalasan Fajar" on the Noice application. The research method used is qualitative descriptive with data collection techniques such as interviews, observations, and documentation. The paradigm employed in this research is post-positivism. The subjects of this study are the production team of the "Catatan Pembalasan Fajar" audio series on the Noice application, including the producer (key informant) and audio engineer (informant). The object of this research is the "Catatan Pembalasan Fajar" audio series on the Noice application. After conducting the research using the program strategy proposed by Peter Pringle, which includes program planning, program production, program execution, program supervision, and program evaluation, the researcher found that the production team implemented several strategies to retain listeners of the "Catatan Pembalasan Fajar" audio series on the Noice application. These strategies include selecting the thriller genre due to high demand from listeners, using bold and assertive sound design, creating an engaging storyline, and paying attention to interactions and listener interests through the comment section of each episode. Despite facing production challenges, the production team successfully created a program that attracted listeners' interest and provided a satisfying audio experience.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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