MétaCan
Menu
Back to cohort
Record W2746208386 · doi:10.17239/l1esll-2012.02.06

Participation in literature and content subject classes: Culture, ethnicity and social space(s)

2012· article· en· W2746208386 on OpenAlexaffabout
Yamin Qian, Tan Yao Xiong

Bibliographic record

VenueL1 Educational Studies in Language and Literature · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSubject (documents)Space (punctuation)CurriculumSociocultural evolutionEthnic groupContent (measure theory)Social spaceQualitative researchMathematics educationPedagogyContent analysisPsychologySociologyLinguisticsComputer scienceSocial scienceMathematicsAnthropology

Abstract

fetched live from OpenAlex

This qualitative case study discusses six English Language learner (ELL) adolescents' experiences of language use in ESL classes, English Literature classes and content subject classes (i.e., Math and Science) in high schools of Toronto, Canada. We found that participants perceived English Literature classes as a social space of “others” where they were more likely to keep silent for several reasons. In contrast, ESL classes and content subject classes were considered as a social space of “ours” within which they participated more actively with hybrid forms of language use and sociocultural practice. This article links the findings to the nature of social spaces and language use. In particular, the content and interaction in classroom activities are explored, which form multiple social spaces for language use and impacts learning outcomes. The study concludes with a discussion on content, interaction and local practice in the school curriculum to enhance second language learning.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0150.010
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.110
GPT teacher head0.504
Teacher spread0.393 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueL1 Educational Studies in Language and LiteratureSame topicMultilingual Education and PolicyFrench-language works237,207