Italian Nostalgia: National and Global Identities of the Italian Novel
Bibliographic record
Abstract
The production, distribution and consumption of the Italian novel in the global editorial market has accompanied the gradual creation of a national branding process that does not coincide with the literary identity Italy tends to associate with. When considering the main analysis approaches of the Italian literary canon—the top-down one of the country’s yearly national bestsellers, literary prizes, suggested readings in Literature courses at University and school anthologies—and the larger-scale, bottom-up view of digital platforms like Amazon and Goodreads, Wikipedia stands in a middle, unexplored ground between the two. Through the comparative quantitative analysis of data derived from some of these sources, this article aims to gain more awareness on Italian literature from 1980 to 2021, to start addressing why national book prizes winners do not make it to the global market and if it is possible to talk about a national cultural resistance, which allowed authors like Elena Ferrante and Goliarda Sapienza to become literary sensations abroad before it happening in their own country. While some studies have already dealt with the unchanging aspect and the lack of diversity of the Italian literary canon, there has been little quantitative research on the two brands of the country, the national and the global, and on the dynamics between them. As well as proposing a methodology for the ongoing study of literary reception of Italian novels under multiple points of view, this article contributes to the discussion on the reliability of measures for studying the canon.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".