Interactive Storytelling, User Agency and Emerging Gender Narratives in the Web Space
Bibliographic record
Abstract
Arguably, storytelling has always been part of human existence and has historically played a crucial role in the evolving nature of the society. It could be described as a thriving, highly esteemed art form that enhances community cohesion, assists in learning, preserves memories, and provides amusement. A peek into the historical trend of storytelling reveals a thread of progression with changing times and emerging technologies. But one thing remains constant, the storytelling space has always been a platform for contextualization and interplay of perspectives. Interactive storytelling offers an interesting kind of narrative experience, one in which a story’s unfolding can be influenced by its audience. This study recognizes user agency as a theoretical underpinning of interactive storytelling which allows for the understanding of users’ responses in social media as well as their underlying mechanisms. It leans on the relative theories of Mediamorphosis and Objectification to examine the Honest Bunch Podcast within the framework of coevolution, convergence, and complexity as well as its interception with the framing of gender narratives. Adopting a qualitative content analysis approach, this study explores the relationship between human and technological agency as well as how interactive storytelling presents gender focused narratives as part of our social media experience. It submits that a story can be considered from the perspective of its featured characters or from a more abstract perspective at the global level of the plot while amplifying the narrative experiences with elements of plurality.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".