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Record W2591421311 · doi:10.5070/c362027918

Exploring Canzone Napoletana and Southern Italian Migration Through Three Lenses

2016· article· en· W2591421311 on OpenAlexaboutno aff
John L. Vitale

Bibliographic record

VenueCalifornia Italian Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicItalian Fascism and Post-war Society
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaImmigrationSorrowSoulHistoryLaughterGender studiesPerceptionPerspective (graphical)SociologyLiteratureAestheticsArtPsychologyVisual artsTheologyPhilosophy

Abstract

fetched live from OpenAlex

Rich in history and tradition, canzone Napoletana have been celebrated and venerated around the world. These songs of love, laughter, sorrow, and pain are a genuine and sincere portal into the heart, mind, and soul of millions of Italian immigrants within the Italian diaspora. Henceforth, the purpose of this article is threefold. First, it will address how canzone Napoletana have acutely impacted the Italian diaspora, becoming the metaphorical voice for the majority of Italian immigrants the world over. Second, it will outline how canzone Napoletana have significantly influenced non-Italian perceptions about Italy and Italian culture. Lastly, this article will provide a uniquely Canadian perspective by specifically illustrating the plight of Italian immigrants living in post World War II Toronto and how these immigrants used canzone Napoletana as a coping mechanism for the daily hardships and struggles of immigrant life.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.014
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.149
GPT teacher head0.310
Teacher spread0.161 · 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 designQualitative
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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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