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Record W4323660805 · doi:10.33137/ic.v16i.40337

Seven Best-kept Linguistic Secrets of Italian Canadians

2023· article· en· W4323660805 on OpenAlexaffvenueabout
Jana Vizmuller-Zocco

Bibliographic record

VenueItalian Canadiana · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsYork University
Fundersnot available
KeywordsLinguisticsHistoryPhilosophy

Abstract

fetched live from OpenAlex

The word "secret" does not refer here to something wilfully hidden, but it indicates an unknown or largely undiscovered element, or a topic deemed so far unworthy of study by the academic community.When an inventory of the research conducted on the languages of Italian Canadians is taken, the lion's share of the studies belongs to the description of italiese (or Italo-Canadian, the ethnolect of the first and to some extent second and third generations) both as a linguistic phenomenon and as a keeper and transmitter of identities.This in itself is a first and important step in reaching some understanding of the linguistic experiences of Italian Canadians.The lessons of italiese are clear and they echo the traditional conclusions reached by sociolinguists: firstly, unless an ethnic language, a koine, is supported by its speakers and by the society at large, it will disappear (see Siegel for the general definition of koine; if suggestions in his study are followed, italiese reached only a pre-koine stage).That this is the fate of italiese seems obvious and to dwell on the reasons why words such as morgheggio or yarda or giobba will soon meet their demise are rather obvious, although it is taking longer than Danesi predicted.("Ethnic koines and the Verbal Structuring of Reality: Psycholinguistic Observations on Canadian Italian" 114) It must be noted that Clivio's suggestion of "a new italiese" only makes its disappearance more evident.(Maglio 5) Secondly, language contact phenomena are not just a matter of universally based linguistic rules with differences in the parameters set by the grammar of the two or more languages.The meeting of, let's say, Sicilian and English in Toronto in the early 1950s within the same speaker or within the Sicilian immigrant community resulted in the inevitable borrowing and nativization of English lexemes to fit the Sicilian linguistic boundaries.Nevertheless, it is instructive to underline the fact that at the beginning the borrowed words almost without exception had something to do with survival, work, and consumerism, and until now, again almost without exception their list does not contain abstract terms, in contrast to the numerous English abstract terms borrowed into Italian in Italy (for a partial list of English borrowings into the Canadian italiese, see Danesi,1986 and Iuele-Colilli 1991).Education, political power, and social forces clearly underpin the bor-

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)0.002
metaresearch head score (Gemma)0.005
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 score0.061
Threshold uncertainty score0.446

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0320.025
Scholarly communication0.0080.002
Open science0.0020.008
Research integrity0.0020.004
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 head0.259
Teacher spread0.241 · 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".

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Citations0
Published2023
Admission routes3
Has abstractyes

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