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Record W3097955548 · doi:10.1177/0261927x20966734

Ethnolinguistic Vitality, Identity and Power: Investment in SLA

2020· article· en· W3097955548 on OpenAlexaff
Richard Clément, Bonny Norton

Bibliographic record

VenueJournal of Language and Social Psychology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsVitalityIdentity (music)IdeologyContext (archaeology)PsychologySocial identity theorySocial psychologySociologyRelevance (law)Representation (politics)LinguisticsIdentity formationEpistemologySocial groupSelf-conceptPoliticsPolitical scienceAesthetics

Abstract

fetched live from OpenAlex

This article concerns the relationship between social context, identity and intergroup relations. It reviews early formulations pertaining to contextual influences and proceeds to examine vitality theory, specifically, ethnolinguistic vitality (EV). The ensuing discussion considers objective aspects of EV such as the demographic representation of a group and delineates multiple influences modulating their impact on intergroup relations. Subjective formulations of EV focusing on how it is perceived are then examined in view of their interaction with intergroup issues such as ideologies, the formation of networks and language loss. While acknowledging the wide conceptual girth of EV, a central intergroup issue remains communication. Following through, issues pertaining to language acquisition are therefore scrutinized as they relate to EV, linguistic identification, and motivation. A parallel is drawn with Bourdeusian approaches, which orient the analysis toward the dynamics of power and investment. Specifically, attention is directed to ideological and identity processes underpinning the learner’s involvement in the learning task. The paper concludes with a discussion of the relevance of the concept of global English as well as the necessity for a framework of contextual factors reaching beyond intrapsychic constructs.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.453
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.119
GPT teacher head0.535
Teacher spread0.416 · 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 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".

Quick stats

Citations33
Published2020
Admission routes1
Has abstractyes

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