The Audio Series Production Team's Strategy for "Catatan Pembalasan Fajar" In Retaining "Noice" Application Listeners
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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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".