Popular Fiction and the ‘Emotional Turn’: The Case of Women in Late Victorian Britain
Bibliographic record
Abstract
Abstract Many within the history profession today consider that we are experiencing an ‘emotional turn’, a perception that has been spurred by a recent proliferation of research centres and outpouring of publications exploring the concept of emotion. Interest in this field looks likely to grow, although there are methodological challenges that have yet to be overcome, as, of course, there are with any newly emerging field of study. One main concern is source material. Attempting to access such an elusive and intensely subjective area of historical inquiry as emotions requires seeking out new sources, as well as returning to old ones with a fresh eye, with new questions in mind. In the specific realm of the emotional lives of women living in Victorian and Edwardian Britain, fiction proves a promising source – popular fiction especially. This is due to the fact that this was the era that ushered in the modern bestseller, novels that more often than not explored the everyday and the emotional, novels that were thought to have been ‘devoured’ by women in particular. This essay plots recent developments in the burgeoning area of emotions history, as well as those that have taken place in relation to the use of fiction as evidence in a history of women’s interior lives. It argues that, at this point in the development of emotions history, when questions of methodology, interdisciplinarity and sources are being addressed more widely, consideration should be given to popular fiction as a readily available pathway, if not an uncomplicated one, into the emotions of the past.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".