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Record W3022463549 · doi:10.1075/lab.17080.she

A classification of receptive bilinguals

2020· article· en· W3022463549 on OpenAlexaff
Marina Sherkina-Lieber

Bibliographic record

VenueLinguistic Approaches to Bilingualism · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsCarleton University
Fundersnot available
KeywordsComprehensionNeuroscience of multilingualismPsychologyMultilingualismLinguisticsComputer scienceCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

Abstract The term ‘receptive bilingualism/multilingualism’ is used for diverse populations, all of which understand a language without producing speech in it, but differ in the way this receptive ability was achieved and in the linguistic knowledge underlying it. In previous studies, not enough attention is given to the differences between types of receptive bilinguals (RBs); however, a thorough analysis of all types is necessary to understand the nature of receptive bilingualism and, consequently, language comprehension and production in general. I propose a classification of RBs based on the presence and nature of an acquisition process that led to receptive abilities. In this classification, RBs who comprehend a language mutually intelligible with one they know are distinguished from RBs with acquired knowledge. Within the former, RBs with and without previous exposure are distinguished. Within acquired types, RBs who comprehend a heritage language are distinguished from RBs who comprehend a second/foreign language.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.260
GPT teacher head0.334
Teacher spread0.074 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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