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Record W1500688904 · doi:10.15353/joci.v8i1.3057

“Getting Turned On”: Using ICT Training To Promote Active Ageing In New York City

2012· article· en· W1500688904 on OpenAlexvenueno aff
Paula Gardner, Tom Kamber, Julie Netherland

Bibliographic record

VenueThe Journal of Community Informatics · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsSocial connectednessActive ageingInformation and Communications TechnologySocial engagementThe InternetTraining (meteorology)GerontologyCivic engagementPsychologyPublic relationsSociologyMedical educationPolitical scienceOlder peopleSocial psychologyMedicineComputer scienceSocial scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The purpose of this mixed-method study was to examine how participation in a senior’s technology training program influences the social environments and active ageing of older New Yorkers. Findings demonstrate increased and sustained use, improved ability and confidence with computer and Internet technology, and a substantial and positive effect on social connectedness, access to information, and social and civic participation among participants. Authors conclude with a discussion of how comprehensive, community-based ICT training programs (such as OATS) can support the ongoing engagement and re-engagement of older adults within society by building, maintaining and restoring their place within their various communities.

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)0.002
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.133
GPT teacher head0.355
Teacher spread0.222 · 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

Citations35
Published2012
Admission routes1
Has abstractyes

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