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Examining the Integration of Borrowed Nouns in Immigrant Speech: The Case of Canadian Greek

2020· book-chapter· en· W4241123020 on OpenAlexaboutno aff
Angela Ralli, Vasiliki Makri

Bibliographic record

VenueEdinburgh University Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsNounInflectionImmigrationModern GreekPreferenceHistoryLoanConsonantMathematicsStatisticsEconomicsPhilosophy

Abstract

fetched live from OpenAlex

The chapter examines borrowing and integration of nouns in the language spoken by Greek immigrants in Canada, where English is the donor language and Greek the recipient. It deals with the questions whether the typological distance between the English and the Greek, where the former is analytic and the latter fusional is an inhibitor for borrowing and whether the types of integration can be attributed to specific properties of the languages in contact. It argues that phonological, morphological and semantic factors are at work throughout the process of adopting and integrating English nouns, but morphological constraints have the most prominent role. More specifically, it shows the mandatory alignment to the fundamental Greek properties of inflection and gender assignment, which caters for the accommodation of loan nouns in Canadian Greek by assigning them specific gender values, and an unequivocal preference for particular inflection classes, the ones most productively used in Greek. The data are drawn from both written and oral sources, the latter being recorded interviews with spontaneous Greek immigrant speech from the provinces of Québec, Ontario, Alberta and British Columbia.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.944
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.126
GPT teacher head0.322
Teacher spread0.196 · 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 teacher head, not a consensus.

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

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
Published2020
Admission routes1
Has abstractyes

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