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Record W2346388674 · doi:10.1145/2851581.2886431

Rethinking Mobile Interfaces for Older Adults

2016· article· en· W2346388674 on OpenAlexaff
Neil Charness, Mark Dunlop, Cosmin Munteanu, Emma Nicol, Antti Oulasvirta, Xiangshi Ren, Sayan Sarcar, Chaklam Silpasuwanchai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research Council
KeywordsMantraSocial isolationMobile deviceDigital divideComputer scienceInternet privacyOlder peopleIsolation (microbiology)GerontologyWorld Wide WebPsychologyMedicineThe Internet

Abstract

fetched live from OpenAlex

This SIG advances the study of mobile user interfaces for the aging population. The topic is timely, as the mobile device has become the most widely used computer terminal and at the same time the number of older people will soon exceed the number of children worldwide. However, most HCI research addresses younger adults and has had little impact on older adults. Some design trends, like the mantra "smaller is smarter", contradict the needs of older users. Developments like this may diminish their ability to access information and participate in society. This can lead to further isolation (social and physical) of older adults and increased widening of the digital divide. This SIG aims to discuss mobile interfaces for older adults. The SIG has three goals: (i) to map the state-of-art, (ii) to build a community gathering experts from related areas, and (iii) to raise awareness within the SIGCHI community. The SIG will be open to all at CHI.

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)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0060.009
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.003

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 head0.017
GPT teacher head0.297
Teacher spread0.280 · 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 designObservational
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

Citations8
Published2016
Admission routes1
Has abstractyes

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