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Record W4403025119 · doi:10.1515/multi-2024-0024

LINCing learners to digital literacy: supporting social integration and English language learning during COVID-19

2024· article· en· W4403025119 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMultilingua · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsBurnaby HospitalUniversity of British Columbia
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)LiteracyLanguage acquisitionPsychologyLinguisticsSociologyComputer sciencePedagogyMathematics educationMedicinePhilosophy

Abstract

fetched live from OpenAlex

Abstract Many newcomers to Canada experience significant difficulties adjusting to life in their new community, with few more challenging than learning English. While Canada’s Language Instruction for Newcomers to Canada (LINC) program suggests a pathway to social integration, ideologies pertaining to language and diversity that inform the LINC program can lead to the assimilation and marginalization of immigrant and refugee newcomers. The disruptions that COVID-19 brought to LINC classes exacerbated these issues. Here, we explore these themes in an ethnographic study of one LINC site and suggest that the incorporation of digital technologies could offer a space for a translingual pedagogy to take root. With appropriate guidance, the adoption of a translingual pedagogy could work against the problematic discourses perpetuating within LINC and improve English learning outcomes by providing increased opportunities for digital literacy socialization.

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.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.308
Teacher spread0.290 · 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