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Record W4390820078 · doi:10.5772/intechopen.114035

Daily Use of Digital Literacy among Young People with Intellectual Disabilities: A Capability Approach Study

2024· book-chapter· en· W4390820078 on OpenAlexfundno aff
Marie-Ève Boisvert, Delphine Odier-Guedj, Floriane Moulin, Marie-Eve Lefebvre

Bibliographic record

VenueEducation and human development · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReading (process)LiteracyContext (archaeology)EthnographyDigital literacyPsychologyIntellectual disabilityQualitative researchPedagogyMathematics educationSociologySocial sciencePolitical science

Abstract

fetched live from OpenAlex

Although recent studies have highlighted young people with intellectual disabilities’ (ID) difficulties with reading and writing, it is not well-know how these skills are used in their daily life and, even less, regarding their digital literacy. Consequently, the aim of this study was to describe the daily use of digital literacy among adolescents, aged 15–21 years, with ID. An additional aim was to identify the factors that facilitated or hindered the various reading and writing practices beyond the classroom context, including home and other familiar spaces like the grocery store. To do so, the digital literacy practices of two adolescents were identified through an ethnographic multi-case study by using creative methods to generate qualitative data. Through Sen’s and Nussbaum’s capability frameworks, it was found that these two young people employed digital literacy through applications on mobile phones and electronic tablets. They developed different capabilities and functionings, particularly “affiliation” and “senses, imagination, and thoughts”. The discussion section of this study focuses on how daily- and school-based digital literacy usages can be bridged to support young people with IDs’ learning and engagement at school.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.033
GPT teacher head0.286
Teacher spread0.253 · 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.

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
Published2024
Admission routes1
Has abstractyes

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