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Record W2275427225 · doi:10.1080/21565503.2015.1050411

Social media and senior citizen advocacy: an inclusive tool to resist ageism?

2015· article· en· W2275427225 on OpenAlexaff
Barry Trentham, Sandra Sokoloff, Amie Tsang, Sheila M. Neysmith

Bibliographic record

VenuePolitics Groups and Identities · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPublic relationsSocial mediaParticipatory action researchCitizen journalismSocial engagementPolitical sciencePopulationSociologyFocus groupSocial science

Abstract

fetched live from OpenAlex

With population aging, interest groups demand that governments act to prevent a perceived financial crisis. Senior citizens remain frustrated in their efforts to influence the response of policy-makers. In an effort to strengthen their voice, one group of senior citizens, engaged in a participatory action research project, questioned how online social media could be used in their advocacy efforts. This query led to an examination of the literature with the primary objective of determining what is known about the use of social media by senior citizens for the purposes of social advocacy. The outcomes of the review revealed that very few studies specifically examined this question. Senior citizen online roles were depicted as consumers of health information or socializers with family and friends. Ageist assumptions informed the design of computer hardware, online formats and norms for social engagement. Senior citizens have concerns about the trustworthiness of social networking sites and while social media can exclude senior citizens from public debate, the authors conclude that the pressing issue is to focus on age-friendly design and supports. With these in place, social media can provide a venue for senior citizens to challenge ageism and influence public policy discourses.

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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.312
Teacher spread0.286 · 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

Citations55
Published2015
Admission routes1
Has abstractyes

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