Bibliographic record
Abstract
In the first systematic study about the Italian detective novel (1979), Loris Rambelli dates the beginnings of the genre to 1929, the publication year of the first of publisher Mondadori's ‘Yellow Books’ (Libri Gialli), the series of yellow-covered books which made the ‘giallo’ synonymous with a crime novel. Nonetheless, texts dealing with mysteries, criminals, police, trials and detection enthralled Italian readers from the 1850s on, complying with the modern dynamics of mass phenomena, contributing to the modern conception of the genre, and playing a crucial role in the culture and society of a recently unified Italy. Not conforming to a recognizable genre-structure, the pre-1929 period has been defined the “prehistory of Italian crime fiction” or protogiallo and has become a topic of academic interest only in recent years. The newness of the scholarship explains the methodological difficulties researchers have to face, such as the classification problem – it is very complex to establish common critical criteria for analyzing diverse materials such as feuilletons, novels, short stories and famous trials journals – and the objective delay in the development of the genre in Italy, especially compared to the British, American and French cases. Building on the recent line of investigation, this paper examines such critical issues in order to identify a methodological approach and a theoretical framework useful to study the prehistory of Italian crime fiction.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".