Immersion in Digital Fiction
Bibliographic record
Abstract
In this article, we profile an empirically grounded, cognitive approach to immersion in digital fiction by combining text-driven stylistic analysis with insights from theories of cognition and reader-response research. We offer a new analytical method for immersive features in digital fiction by developing deictic shift theory for the affordances of digital media. We also provide empirically substantiated insights to show how immersion is experienced cognitively by using Andy Campbell and Judi Alston’s (2015) digital fiction piece WALLPAPER as a case study. We add ‘interactional deixis’ and ‘audible deixis’ to Stockwell’s (2002) model to account for the multimodal nature of immersion in digital fiction. We also show how extra-textual features can contribute to immersion and thus propose that they should be accounted for when analysing immersion across media. We conclude that the analytical framework and reader response protocol that we develop here can be adapted for application to texts across media.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".