Bibliographic record
Abstract
This chapter provides an overview of the ways in which the boom of digital technologies has affected the novel. The Internet and especially social media are now recurrent themes in print fiction, which also often reflect the changes in our experience of space and time through new structures and styles. Beyond such thematic manifestations, however, the novel has seen more fundamental innovations that stretch its traditional boundaries. We can discern three main areas of evolution. First, the emergence of new modes of publication, including digital publishing, self-publishing, and writing platforms such as Wattpad have democratized the access to audiences and incited amateurs to write fiction. Secondly, the new modes of communication facilitate the exchange between authors and readers, while also bringing about the rise of the ‘influencers’, who are taking over the role of trend-setting from professional critics. Lastly, new modes of storytelling have emerged that rely on digital networks: interactive fictions that break up the linearity of the text and give agency to the reader, and blogs, websites, and social media experiments that play with temporality, form, and modes of interaction with the audiences.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".