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Record W4386053251 · doi:10.1515/9783110600926-028

28 Language contact and linguistic areas

2023· book-chapter· en· W4386053251 on OpenAlexaboutno aff
Sarah G. Thomason

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsPidginLanguage contactLinguisticsIndigenousJargonSection (typography)GeographyStress (linguistics)HistoryImmigrationFocus (optics)EthnologyCreole languageArchaeology

Abstract

fetched live from OpenAlex

This chapter surveys the extent and nature of language contacts in North America north of Mexico-that is, in Canada and the continental United States. The chapter begins with an introductory survey of multilingual contacts on the continent- who is (or was) multilingual, where multilingualism exists (or existed), and when contacts occur(red). Next, the focus is on contacts among indigenous peoples, especially in pre-European, pre-reservation/reserve days. The chapter then moves on to a consideration of indigenous people’s contacts with Europeans, in particular immigrants from Spain, France, England, and Russia. The next section describes some of the non-extreme linguistic results of contacts: lexical borrowing and resistance to it, and structural diffusion. A separate section is devoted to a closer look at North American mixed languages and their histories: the pidgins Chinook Jargon, Pidgin Delaware (Lenape), Mobilian Jargon, American Indian Pidgin English, the Plains Indian Sign Language, and a few others; and the bilingual mixed languages Michif and Mednyj Aleut. The final main section covers linguistic areas in North America, in particular the Pacific Northwest, Northern California, and the Southeast.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.035
GPT teacher head0.315
Teacher spread0.281 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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