Immersion, digital fiction, and the switchboard metaphor
Bibliographic record
Abstract
This paper re-evaluates existing theories of immersion and related concepts in the medium-specific context of digital-born fiction. In the context of our AHRC-funded “Reading Digital Fiction” project (2014-17) (Ref: AH/K004174/1), we carried out an empirical reader response study of One to One Development Trust’s immersive three-dimensional (3D) digital fiction installation, WALLPAPER (2015). Working with reading groups in the Sheffield area (UK), we used methods of discourse analysis to examine readers’ verbal responses to experiencing the installation, paying particular attention to how participants described experiences pertaining to different types of immersion explicitly and implicitly. We explain our findings by proposing the idea of a switchboard metaphor for immersive experiences, comprising layers and dynamic elements of convergence and divergence. Resulting from our analysis, we describe immersion as a complex, hybrid, and dynamic phenomenon. We flag the need for a more discriminating treatment of specific types of immersion in medium-specific contexts, including a distinction between literary and narrative immersion, and collaborative and social immersion (Thon 2008). We argue that literary immersion is needed as a separate immersive category because it differs from narrative immersion, and is far more linked to the activity of cognitive word processing. Similarly, we introduce collaborative immersion as an additional immersive category to reflect attention shifts towards site-specific, human interactions. Finally, our data shows the importance of site-, situation-, and person-specific constraints influencing reader-players’ ongoing ability to establish and retain immersion in the storyworld.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".