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Weaving family connections on-and offline: the turn to networked individualism

2018· book-chapter· en· W2914045862 on OpenAlexaboutno aff
Anabel Quan‐Haase, Hua Wang, Barry Wellman, Renwen Zhang

Bibliographic record

VenuePolicy Press eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsSocial connectednessIndividualismFeelingSociologyInformation and Communications TechnologyInterpersonal tiesICTSPerspective (graphical)Social psychologyPublic relationsPsychologyPolitical scienceSocial scienceWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

This chapter examines the role of information and communication technologies (ICTs) in family life from the perspective of older adults, and whether ICT use is helping to maintain and strengthen social ties within and across generations. Drawing on networked individualism as a conceptual and analytical model, it investigates social and network transitions affecting families and communities since the 1990s. In-depth interviews conducted in 2013–2014 with older adults from East York, Toronto, show that most of them rely more on email and less on social media and video chat. Most importantly, they still prefer spending time in person. The chapter also considers how the so-called Triple Revolution in how society operates has created opportunities for a transition to networked individualism that affects family interactions. Finally, it discusses three areas where ICTs have been integrated into family life: practices of connectivity, maintaining family ties near and far, and feelings of connectedness.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.010
Scholarly communication0.0050.007
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.064
GPT teacher head0.328
Teacher spread0.264 · 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 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

Citations15
Published2018
Admission routes1
Has abstractyes

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