Bibliographic record
Abstract
This paper critically examines the concept of 'entanglements' in digital storytelling, particularly in refugee narratives, where the intricate interplay between digital technologies, audience participation, and narrative structures presents unique ethical complexities. In an era where digital media—from social platforms to virtual reality—has transformed traditional storytelling, this research explores the impact of these interconnected elements on the portrayal and audience engagement with real-life characters, specifically refugees. The study examines the multifaceted relationships inherent in digital storytelling, probing into the ethical challenges and responsibilities arising when digital narratives entwine refugee experiences. It addresses how storytellers balance creative freedom with respectful representation and the ethical dilemmas encountered in formats like interactive films and ARGs that involve real experiences and identities. Central to this investigation is the participatory authorship, as Sandra Gaudenzi and Kate Nash discussed, and its implications in refugee storytelling. This research examines how online communities and interactors contribute to narrative entanglements, potentially democratizing storytelling while risking misrepresentation and ethical breaches. However, the interactive format also has its limitations. Unlike traditional documentaries with a more standardized and widely recognized format, i-docs can be less familiar to audiences and may require more effort to access and engage with. This can limit their reach and impact, especially if they are not widely promoted or integrated into mainstream programming. As a result, there is a need for more awareness and education about the i-doc format and its potential for storytelling and greater investment in their production and distribution. By doing so, we can unlock the full potential of this innovative and powerful medium and provide a platform for marginalized communities to tell and share their stories in their own voices. The analysis will focus on projects, such as Pushbacks Across the Evros (2013-present) and Liquid Traces (2017) from the interdisciplinary research agency Forensic Architecture, based at Goldsmiths, University of London. The agency established a ground-breaking initiative that employs cutting-edge techniques to expose human rights violations and promote social justice. Also, the role of immersive technologies like VR and AR in storytelling ethics. The paper questions whether these technologies enhance or complicate ethical storytelling, especially in refugee narratives. It posits that while such technologies provide new immersive dimensions, they introduce narrative integrity and ethical portrayal challenges. This paper examines a nuanced perspective on digital narrative entanglements. It argues that digital media's capacity for innovative storytelling is accompanied by complex ethical challenges, especially when involving real characters like refugees. These entanglements demand a refined approach to storytelling that respects the complexities of refugee experiences, promoting ethical representation and audience engagement. By integrating critical analysis of contemporary scholarly viewpoints, the paper aims to understand comprehensively how digital media reshapes storytelling. It emphasizes the need for a balanced approach that fosters innovation and participation while upholding ethical standards in narrative creation and consumption, especially in refugee narratives. The study underscores the importance of enhancing media literacy to empower audiences and participants to navigate and interpret these entangled realities critically, fostering a more inclusive and ethically responsible narrative landscape.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".