Buine fortune: L’emigrazione friulana nel secondo dopoguerra
Bibliographic record
Abstract
according to the journal, to an Italian paradigm of "ethnic studies" in the United States.[I have dealt with this in my Una semiotica dell'etnicità (Franco Cesati Editore, 2010) 23 sgg.]We would not err, therefore, to state the same for Oltreoceano and the organization of which it is the official publication.The "Centro Internazionale Letterature Migranti" (CILM), we read, "accoglie studi di carattere letterario, linguistico e culturale sulle comunità migranti d'oltreoceano -friulane in partico-lari…" and while it may parenthetically state "friulane in particolari," we might indeed note that (a) there are indeed Friulians in the United States, and (b) not everyone in this issue is Friulian.Furthermore, of the sixteen people on the Comitato Scientifico (Editorial Board), who represent a broad spectrum of European and "American" scholars, not one is from the United States.Namely, the country to which the largest number of Italians migrated, especially during the high wave of emigration, in the late nineteenth and early twentieth century, is not at all recognized by the Centro Internazionale Letterature Migranti.Such a blatant breach of exclusion only brings to the surface the problematic lacunae that still undergird what we can only assume is an Italian dominant cultural mode of thinking rooted still in a mindset coincidental to the likes of Giuseppe Prezzolini and Mario Soldati.As in the case of Àcoma's special issue of "letterature degli Stati Uniti," Oltreoceano, or, better still, CILM, suffers from a similar glaring blind spot that only devalues the otherwise valuable contributions to this special issue.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".