The Interlocutor in Print and Digital Fiction: Dialogicity, Agency, (De-)Conventionalization
Bibliographic record
Abstract
Digital fiction typically puts the reader/player in a cybernetic dialogue with various narrative functions, such as characters, narrative voices, or prompts emanating from the storytelling environment. Readers enact their responses either verbally, through typed keyboard input, or haptically, through various types of physical interactions with the interface (mouseclick; controller moves; touch). The sense of agency evoked through these dialogic interactions has been fully conventionalized as part of digital narrativity. Yet there are instances of enacted dialogicity in digital fiction that merit more in-depth investigation under the broad labels of anti-mimeticism and intrinsic unnaturalness (Richardson, 2016), such as when readers enact pre-scripted narratees without, however, being able to take agency over the (canonical) narrative as a whole (Dave Morris’s Frankenstein), or when they hear or read a “protean,” “disembodied questioning voice” (Richardson, 2006: 79) that oscillates between system feedback, interior character monologue and supernatural interaction (Dreaming Methods’ WALLPAPER). I shall examine various intrinsically unnatural examples of the media-specific interlocutor in print and digital fiction and evaluate the extent to which unconventional interlocutors in digital fiction may have anti-mimetic, or defamiliarizing effects.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".