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Record W4394975244 · doi:10.47862/apples.137177

Reviewing research methods on adult migrants’ digital literacy

2024· article· en· W4394975244 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.

Bibliographic record

VenueApples - Journal of Applied Language Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsDigital literacyCurriculumLiteracyInclusion (mineral)Information literacyDigital inclusionSociologyDigital mediaPedagogyComputer scienceSocial scienceWorld Wide WebThe Internet

Abstract

fetched live from OpenAlex

This article presents a selective literature review covering the period from 2016 to 2023, focusing on research published in peer-reviewed journals, to examine the methodologies employed in investigating the digital literacy of adult migrants and refugees. Three distinct approaches emerged: digital use study, ethnography, and pedagogical experimentation and intervention. These methods offer unique perspectives and complement each other in exploring how digital literacy can empower migrants to actively engage in the evolving digital landscape and facilitate language learning. The findings from a subset of 14 studies included in this review were categorized into a digital literacy taxonomy, aiming to inform language teaching practices tailored to the needs of migrants. This research addresses the urgent need for adapting language teaching and curricula in host countries to accommodate the increasing global migration and digitalization of learning. Additionally, suggestions for future research directions are provided to gain a deeper understanding of the specific digital literacy needs of this population and enhance the linguistic skills and social inclusion of newcomers.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.248
GPT teacher head0.655
Teacher spread0.407 · 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