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Record W2170482537 · doi:10.1080/13532940500489510

Global Geography of ‘Little Italy’: Italian Neighbourhoods in Comparative Perspective

2006· article· en· W2170482537 on OpenAlexaboutno aff
Donna R. Gabaccía

Bibliographic record

VenueModern Italy · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationCeltic languagesEthnologyEmpireIdeologyGeographyRace (biology)Perspective (graphical)SociologyHistoryPoliticsPolitical scienceArchaeologyGender studiesLawArt

Abstract

fetched live from OpenAlex

Between 1870 and 1970 the migration of 26 million people from Italy produced an uneven geography of Little Italies worldwide. Migrants initially clustered residentially in many lands, and their festivals, businesses, monuments and practices of everyday life also attracted negative commentary everywhere. But neighbourhoods labelled as Little Italies came to exist almost exclusively in North America and Australia. Comparison of Italy's migrants in the three most important former ‘settler colonies’ of the British Empire (the USA, Canada, Australia) to other world regions suggests why this was the case. Little Italies were, to a considerable extent, the product of what Robert F. Harney termed the Italo-phobia of the English-speaking world. English-speakers’ understandings of race and their history of anti-Catholicism helped to create an ideological foundation for fixing foreignness upon urban spaces occupied by immigrants who seemed racially different from the earlier Anglo-Celtic and northern European settlers.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0040.007
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.290
Teacher spread0.272 · 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 designObservational
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

Citations27
Published2006
Admission routes1
Has abstractyes

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