Kunskap + underhållning = framtidens informationsförmedling? : En multimodal analys av kunskapsförmedling i musikvideor
Bibliographic record
Abstract
Anchored in affect theory, how power is understood through feelings rather than logic (Schaefer 2016), and civic imagination, the desire to create shared spaces to communicate hopes for the future (Jenkins, Peters-Lazaro & Shresthova 2020, s. 183), this paper aims to discuss two questions: How are political and/or social messages reproduced in entertainment media? What effect does it have to implement political messages and/or social issues in entertainment related media content? Two music videos from Taylor Swift’s discography will be analyzed through a multimodal analysis: The man (2020), which addresses the gender equality issue, and You need to calm down (2019), which addresses hatred directed towards the HBTQ+-community. Edutainment, education through entertainment, is understood to be a good way of achieving attitude changes in society, since it targets the audience’s emotions rather than their rational mind, which is a more effective way to create engagement (Usdin, Scheepers, Goldstein & Japhet 2005). The media habits of younger generations are understood to adhere foremost to the individual’s interests, and media content is evaluated by its ability to entertain or its use for personal development (Boczkowski, Mitchelstein & Matassi 2018; Galan, Osserman, Parker & Taylor 2019). This paper concludes that infotainment/edutainment imitates current discourses as well as challenge these from the perspective of civic imagination and the thought of a better future, with the help of storytelling through characters that are meant to evoke interest and engagement through emotions.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".