Kinetic - an Exploration of Storytelling Media and Content Experiences and the Impact on Fan Engagement
Bibliographic record
Abstract
The music industry is rapidly changing, with technology affecting music production, consumption, and promotion. Digital storytelling has demonstrated an impact on the success of music artists and their work, affecting relationships and environments between artists and audiences to become more dynamic. Media users now have access to a plethora of content, and contemporary media studies have begun to take a multi-dimensional approach when analyzing media effects (Auter & Palmgreen, 2000). Yet, past literature has focused on analyzing specific mediums, such as television and radio, and media outcomes individually and separately (A. M. Rubin et al., 1985). Thus, new research studies that compare multiple mediums, such as video and virtual reality, and media effects, in an integrated context including concepts such as parasocial interaction, identification, affinity, similarity, and imitation, will provide further insights that are more representative of the modern media consumption process. This research asks : “Do digital storytelling experiences affect the relationship between artist and audience in the music industry?”. Specifically, it aims to interrogate media consumption outcomes of parasocial interaction, identification, similarity, affinity, and imitation at the developmental stage between media figures and media users. In a study of 89 participants, the results indicate significant differences between various media, with video and text mediums showing the strongest positive influences on participants in respect to the selected media outcomes. It also suggests correlations between media factors, supporting the direction of multi-dimensional analysis of media outcomes. The study proposes several considerations for media characters and brands relevant to the process of storytelling content optimization based on audience uses and gratifications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".