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Record W2984634333 · doi:10.1093/geroni/igz038.1367

“BUT WE ARE NOT POWERLESS AGAINST THIS PROBLEM”: OLDER PEOPLE, LONELINESS AND THE MEDIA

2019· article· en· W2984634333 on OpenAlexaffabout
Mary Pat Sullivan

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsNipissing University
Fundersnot available
KeywordsLonelinessShameIdeologyPerceptionFraming (construction)PopulationPopulation ageingSocial psychologySociologyStigma (botany)PsychologyPublic relationsPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Abstract Media campaigns play a critical role in framing public perceptions or ‘public talk’ around social issues. The media’s role in characterizing the loneliness ‘problem’ is, however, an under explored area. This paper presents the language of loneliness and loneliness representations in the media in Canada and England over a 10-year period (2009-2018) and their relationship with key policy initiatives specific to an ageing population. Using qualitative content analysis, the findings illustrate the use of skilled marketing techniques and highly stigmatizing discourse. These media approaches act to: (1) reinforce the threat of an ageing population; (2) endorse responsibilization and governmentality of the body; and (3) promote individual and/or family shame and morally responsible actions by charities and volunteers. We conclude that there is a need for a critical analysis of loneliness from the perspective of social and cultural constructions of ageing, the positioning of older people in society, and neo-liberalist ideology

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)0.003
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.018
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.339
Teacher spread0.305 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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