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Record W4400878293 · doi:10.1080/15228835.2024.2376552

Applying Nancy Fraser’s Framework on Three Dimensions of Justice in the Understanding of Justice in the Use of Technology with Older Adults with Moderate to Severe Dementia in Care Settings: Closing the Digital Divide

2024· article· en· W4400878293 on OpenAlexafffundabout
Karen Lok Yi Wong, Diane Pan, Lillian Hung

Bibliographic record

VenueJournal of Technology in Human Services · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of British Columbia
FundersMitacs
KeywordsClosing (real estate)Economic JusticeDementiaSociologyPsychologyGerontologyMedicineNursingPolitical scienceLaw

Abstract

fetched live from OpenAlex

Access to technology is getting more important in human services with older adults. However, the digital divide exists between older adults and younger people, as well as among older adults from different social groups. To close the digital divide, we need to consider justice in technology for older adults. This article aims to understand how justice is involved in the use of technology with older adults. It conducted a secondary analysis by looking into the data of a larger study about using dementia-friendly videos with older adults living with moderate to severe dementia in care settings, one of the most marginalized older adult populations in Vancouver, Canada. It refers to Nancy Fraser’s framework on the three dimensions of justice, including redistribution, recognition, and representation, as the guiding framework of analysis. It suggests that different dimensions of justice are intertwined with each other. It also suggests that future researchers may consider this framework to guide their understanding of justice in the use of technology with older adults.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.002
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 head0.028
GPT teacher head0.295
Teacher spread0.267 · 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.

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

Citations2
Published2024
Admission routes3
Has abstractyes

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