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Record W4308590758 · doi:10.1017/s0142716422000339

Justice and equity for whom? Reframing research on the “bilingual (dis)advantage”

2022· article· en· W4308590758 on OpenAlexafffund
Gigi Luk

Bibliographic record

VenueApplied Psycholinguistics · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNeuroscience of multilingualismPsychologyCognitive reframingMultilingualismMisattribution of memoryCategorizationCognitionCognitive psychologySocial psychologyLinguisticsCognitive science

Abstract

fetched live from OpenAlex

Abstract The search for the existence and nonexistence of bilingual advantages and disadvantages has become a battleground marked by polarized comments and perspectives, furthering our understanding of neither bilingualism as an experience nor cognition as higher-level mental processes. In this paper, I provide a brief historical overview of research examining the cognitive and linguistic consequences of multilingualism and address the assumptions underlying research exploring the bilingual behavioral difference. I aim to illustrate the sole focus on behavioral (dis)advantage fails to reflect the complexity and dynamicity of people’s bilingual experiences, thereby distracting from understanding bilingualism. Responding to the call of this special issue, I describe the necessity to focus on people when moving toward a just and equitable future for applied psycholinguistic research. Furthermore, I explain why the nuances of bilingualism need to be recognized beyond binary categorization to advance knowledge about bilingualism and its consequences. To avoid unjust misattribution of a behavioral outcome to people’s life experience and to report research findings in a transparent manner, the myopic representation of the terms “bilingual (dis)advantage” should be recognized and reflected on.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.308
GPT teacher head0.494
Teacher spread0.186 · 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.

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

Citations45
Published2022
Admission routes2
Has abstractyes

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