Neo‐Victorian f(r)iction: Critical conversations and fictional narratives
Bibliographic record
Abstract
Abstract This article explores how neo‐Victorian critics are drawn to investigate the f(r)iction between the past and to the present with a desire to understand the Victorian past, then to articulate its importance to the postmodern present. Articles in this special issue by Sarah E. Maier, Anna Jones, Marie‐Louise Kohlke, and Mark Llewellyn seek to widen the scope of these conversations, to find other neo‐Victorianisms across the globe and investigate narratives in manga, television, and true crime. The aim is to question how boundaries between high and low break down and consider what happens when intellectual rigour meets non‐traditional genres found within Neo‐Victorian narratives.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.028 | 0.100 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".