Adapting news video narration to online services
Bibliographic record
Abstract
This article examines the motivations, advantages, and challenges of implementing a novel narrative strategy for online news video: the Batman Affective Structure (BAS). The BAS prioritizes emotional engagement as a response to the platformization of news and shifting audience behaviors in digital environments. The model was introduced to three Danish public service organizations – DR, TV 2, and TV 2 Fyn – during a workshop to explore its applicability in newsrooms. Follow-up collaboration with these industry partners examined both its benefits and the tensions it creates within journalistic practice. Findings indicate that public service broadcasters have implemented the BAS to enhance audience engagement and strengthen their presence on streaming platforms. However, the transition also uncovered challenges, such as journalistic resistance to narrative standardization, difficulties in applying the BAS across different news genres, and the evolving role of the studio anchor. The study shows both the strategic potential of the BAS for public service news and the complexities of aligning emotional narrative structures with public service values in a rapidly transforming media environment.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".