Immersive Multi-Screen Journalistic Narratives
Bibliographic record
Abstract
To build representations and meanings, telejournalism in the context of transmediation has relied on the interrelation of different languages, adapting itself according to the emergence of resources and interfaces. In this context, one can cite the exploration of 360-degree audiovisual narratives, an emerging image modality used by the press as a mechanism to bring spectators closer to events. In Brazil, 360-degree journalistic productions made by communication companies, in general, go beyond the television space and are also explored in virtual social networks and websites to instigate the participation of the enunciatee through access to interactivity resources. The present investigation focuses on this articulation process between TV and the Internet, intending to point out reflections on the language of 360-degree audiovisual content in journalism, proposing to analyze discursive strategies and technical specificities of productions disseminated through television support and the online environment. To this end, the series of 360-degree reports “O Vírus na Favela” was examined, launched in 2020 by the program Balanço Geral RJ (Record TV Rio - Brazil) and which sought to portray challenges faced by residents of communities in Rio de Janeiro during the COVID-19 pandemic. This exploratory study is guided by a methodological path formed by a bibliographic survey, based on discussions such as telejournalism, interactivity and 360-degree narratives, and on the analysis of the corpus based on three principles of French semiotics presented by Barros (2005, 85): “narrative syntax,” “discursive syntax” and “discursive semantics.” In addition, the semiotic analysis method for 360-degree films developed by Moreira (2020) was applied. In conclusion, it can be recognized that the analyzed series evidences the enunciators' search to follow the contemporary trend of telling stories in more than one media support, as well as creating interactivity with the viewer. Also, it was observed that TV productions have a hybrid proposal based on several languages, such as graphic elements, conventional videos and 360-degree videos. On the other hand, web content, in part, is configured as interactive versions of 360-degree videos shown on TV. Such evidence may signify that television content producers see this image as an accessory innovation in the composition of traditional reports.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".