Bibliographic record
Abstract
The Coda traces fashion’s permeation of the novel genre into our contemporary moment, and contends that the novel’s innovative engagement with digital technology and social media over the past decade has roots firmly in the nineteenth century. Historicizing the advent of Twitter-fiction, the Coda proposes that we recognize in contemporary fiction’s experiments with seriality and hypercurrency an upcycling of narrative forms developed in analogous moments of profound change in the nineteenth century. Twitter-fiction explores the relation of parts and wholes, of individuals and collectivities, in an historical age when digital mediation enables new configurations of publicness, when virtual self-expression is the medium less of identity than main character energy, and when the timeframe of the quotidian has become an inadequate measure of the experience of historical change. The Coda argues that contemporary writers who immerse their fiction in the media and sensibilities of the moment may risk radical obsolescence, but that is precisely the point: they aim to conceptualize the temporality and textures of the immediate present.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.130 | 0.054 |
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".