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Record W2623764809 · doi:10.4018/ijbide.2017070104

Multilingualism, Identities and Language Hegemony

2017· article· en· W2623764809 on OpenAlexaff
Jing Li, Danièle Moore

Bibliographic record

VenueInternational Journal of Bias Identity and Diversities in Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHegemonyMainstreamMultilingualismEthnic groupMandarin ChineseGender studiesSociologyNegotiationPacific islandersPolitical scienceLinguisticsPoliticsPedagogySocial scienceAnthropology

Abstract

fetched live from OpenAlex

This paper presents the findings from a case study of how five post-secondary ethnic multilingual students (three Bai and two Zhuang) at a local university in Southwestern China experience multilingualism and ethnic identities (de)construction and invest themselves in an active negotiation for legitimate membership in mainstream educational Discourses (Gee, 1990, 2012). The authors seek to understand how the perceived hegemony of Mandarin has impacted their social positioning and delegitimized their multilingual assets and ethnic identities in mainstream educational Discourses, and how they managed to negotiate their identities as ethnic multilinguals in different social Discourses. The authors argue that through the legitimate dominance of Mandarin, these students are not merely being positioned as members of a negatively stereotyped ethnic group but also concurrently participating in reconstructing the Mandarin language hegemony in those very Discourses, which runs the risk of further expanding the existing educational inequalities between Han and ethnic minority students..

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.990

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 communication0.0010.002
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 head0.089
GPT teacher head0.497
Teacher spread0.409 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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