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Record W2411849948 · doi:10.21018/rjcpr.2016.1.199

Grannies on the Net: Grandmothers’ Experiences of Facebook in Family Communication

2016· article· en· W2411849948 on OpenAlexafffundabout
Loredana Ivan, Shannon Hebblethwaite

Bibliographic record

VenueRomanian Journal of Communication and Public Relations · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrandparentPsychologySocial mediaInternet privacyDevelopmental psychologySocial psychologyWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

<p>Despite ageist stereotypes about older people’s abilities to engage with information and communication technologies, grandparents are increasingly engaged with digital media. Grandmothers, in particular, are primarily responsible for using of web-based services to communicate with their children and grandchildren (Quadrello et al., 2005). Photos and news from children and grandchildren, especially grandbabies, act as important incentives for grandparents to go online. The purpose of the study, therefore, was to investigate how grandmothers use Facebook to facilitate family communication with children and grandchildren who move far away from home. Semi-structured interviews were conducted with grandmothers living in Romania and Canada, having a Facebook account and relevant family members (children or grandchildren) far from home. Three themes emerged from the data indicating: 1) the tendency to switch between different platforms to facilitate family communication; 2) the relative passive use of Facebook, focusing on photos and quotations as content that trigger emotions; 3) that Facebook usage is influenced by social norms around decency and privacy. Findings suggest that family relationships play a central role in grandmothers’ motivations and behaviours surrounding Facebook use.</p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.282
Teacher spread0.248 · 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 designTheoretical or conceptual
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

Citations50
Published2016
Admission routes3
Has abstractyes

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