Bibliographic record
Abstract
Two historical phenomena have contributed to the process of linguistic and cultural hybridisation, colonialism and migration; these are universal features, but recent mass migrations and the increasing availability of speed communication media have accelerated such a phenomenon, still without changing the basic mechanisms. The colonial activity of European countries has given origin to a number of postcolonial cultural phenomena, starting with the American literatures (USA and Canada, Francophonie in Quebec, the Hispanic American literature) up to the recent Anglo-Indian one etc., to mention just a few. Such rich cultural creativity has exerted a great impact on the very structure of the language. Italian is not well spread as a post-colonial literary language; in fact, few authors come from past-Italian colonies (Lybia, Horn of Africa, Dodecanese, and Tien-Tsin). In addition, the cultural impact of Italian culture and language on the colonised countries has not been as strong as that of other colonial powers; thus, we can hardly speak of an Italian post-colonial literature. On the contrary, many immigrants from countries that have not been colonised by Italians have learned the language and there is good quality literary production of “foreign” authors, often globally referred to as “migrant literature”. This introduces new contents in the attempt to bridge their culture of origin and those of the host country (Italy). Also, from the point of view of the language, some innovation comes from their contribution. The studies of the impact of such a literature on the Italian culture are rather scanty, but there are some groups and journals that convey such features of a new culture, object of illustration in this paper.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".