Bibliographic record
Abstract
This scholarly essay is about research-creation in the field of interactive, immersive and digital non-fiction storytelling. It seeks to shed light and update this approach to research, and to identify ways in which it can be rendered more accessible to both practitioners and researchers. The essay revisits two recent factual narratives—web-documentary Field Trip (2019) and VR experience Myriad (2021), projects in which the authors were directly involved as practitioners. It positions these two digital practices in the body of literature on media innovations, before qualifying them as interactive and immersive documentaries (i-docs). Field Trip is a browser-based 92-minutes documentary taking a deep dive into the history and social struggles around the Tempelhof Field, Berlin’s former airport turned public park. Myriad is a 32-minutes immersive VR experience narrated from the perspective of globally migrating animals, sensitizing the audience to the interplay and interrelationship of all life forms. The authors explain how these projects have, in their respective and singular ways, opened spaces for iterative loops between creative practice and theory. Field Trip serves to exemplify how triangulation can be applied to creative media practice. This method, albeit a relatively classic one in research, continues to be a challenge for practitioners. The case of Myriad, in turn, is used to discuss two additional methods: core to audience and interdisciplinary iteration in experimental aesthetics. While these two methods in format development and artistic enquiry might sound familiar to practitioners, they are promising for closing some gaps in the design of academic research. The two case studies speak to the diversity of research-creation approaches, methods and formats, while at the same illustrating how research-creation, as a research mindset, can facilitate the output of two things at once: a more ‘educated’ artistic expression and more grounded academic knowledge. The essay further identifies the need to systematize and offer continuous support to researchers-creators. It argues that this fits the mandate of higher education art and design schools, which should be understood as research-creation competence centers. The paper ends on five learnings meant to encourage digital media practitioners to develop an enquiry reflex and media scholars to “get their hands dirty” in partaking in innovative creative media projects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".