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Record W2939353787 · doi:10.1080/15348458.2019.1569525

Language Ideology, Christianity, and Identity: Critical Empirical Examinations of Christian Institutions as Alternative Spaces

2019· article· en· W2939353787 on OpenAlexaff
Huamei Han, Manka Varghese

Bibliographic record

VenueJournal of Language Identity & Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsIdeologySalientChristianitySociologySocializationIdentity (music)Power (physics)Symbolic powerEpistemologyPedagogyLinguisticsSocial scienceAestheticsPolitical scienceTheologyPhilosophyLaw

Abstract

fetched live from OpenAlex

The teaching and learning of languages has been mainly investigated within educational institutions, especially by applied linguists. However, religious spaces such as churches and church related programs have historically and contemporarily served as important alternative spaces for such teaching and learning to take place. At the same time, such institutions and the way that language teaching and learning unfold in these spaces necessitates both a critical and empirical examination which makes salient the role and consequences of power. The focus of this special issue is to provide examples of studies which seek to fill this gap. This introduction serves as a way to set up this special issue and the articles within it by making salient the themes of language socialization, language ideology, identity, Christianity, ethnography and systems of power, as well as showing how the four studies in this special issue speak to the aforementioned gap and these themes.

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)0.014
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.006
Science and technology studies0.0170.061
Scholarly communication0.0140.014
Open science0.0020.010
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.526
Teacher spread0.467 · 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 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

Citations22
Published2019
Admission routes1
Has abstractyes

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