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Record W4367849058 · doi:10.33137/ic.v4i.41051

Il Canada degli Anni ’20: realismo e poesia in un resoconto italiano dell’epoca

2023· article· it· W4367849058 on OpenAlexaffvenueabout
Maddalena Kuitunen

Bibliographic record

VenueItalian Canadiana · 2023
Typearticle
Languageit
FieldArts and Humanities
TopicItalian Literature and Culture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesArtHistory

Abstract

fetched live from OpenAlex

resoconto italiano dell'epoca MADDALENA KUITUNEN Quando si vuol discutere il tramonto del mito letterario e popolare dell' America, intesa come un paese di libertà e di abbondanza, nella cultura italiana del Novecento, si risale di solito ad America amara di Emilio Cecchi, la quale, a detta dell'autore nell'avvertimento all'opera stessa, ". . .raccoglie e coordina impressioni e memorie di due soggiorni abbastanza lunghi agli Stati Uniti e al Messico nel 1930-31 e 1937-38."Non si può escludere da questo riconoscimento anche l'autorità acquisita in patria dal Cecchi, quale romanziere e critico letterario, fin dagli inizi del secolo.Per il critico letterario americano Leslie Fiedler, però, il clima politico italiano di quel periodo, di forte propaganda contro le potenze occidentali, accentuatosi nel 1939, anno della prima pubblicazione di America amara era stato il fattore principale della visione negativa del Cecchi.Presentando Cesare Pavese agli Americani, così infatti si esprimeva il Fiedler nei riguardi di America amara e del suo autore: "... his older, pro Fascist contemporary Emilio Cecchi had in a book of essays called America amara (Bitter America) turned a real visit to this country into a fact of the imagination."Ebbene, noi riteniamo che il primato per una visione amara dell' America vada invece ad un altro autore: Arnaldo Cipolla, e ad un altro titolo: Nell'America del Nord, meno poetico, se vogliamo, di quello del Cecchi, ma altret-Mi:

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0220.015
Scholarly communication0.0120.002
Open science0.0010.003
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.012
GPT teacher head0.194
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes3
Has abstractyes

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