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Record W2099846281 · doi:10.1080/21699763.2013.802988

Social networks amongst older people in OECD countries: a qualitative comparative analysis

2013· article· en· W2099846281 on OpenAlexaboutno aff
Philip Haynes, Laura Banks, Michael Hill

Bibliographic record

VenueJournal of International and Comparative Social Policy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsnot available
FundersEconomic and Social Research CouncilQueen Mary University of London
KeywordsQualitative comparative analysisHomogeneousGovernment (linguistics)Social network (sociolinguistics)Qualitative propertyDemographic economicsComparative researchPsychological interventionGeographyPolitical scienceEconomic growthDevelopment economicsSociologyEconomicsSocial sciencePsychologySocial mediaComputer science

Abstract

fetched live from OpenAlex

Using data from The International Social Survey Programme this paper compares the social networks of those aged 50 and above in 18 countries. Two different types of networks are conceptualised: family contact and community participation. Using qualitative comparative analysis (QCA), international sets are established for four groups of countries. Set one includes countries that only satisfy a minimal number of social network thresholds (France, Norway, Great Britain, Denmark and the USA). Set two includes a homogeneous group of countries with above-threshold rates of marriage and community participation (Australia, New Zealand, Germany, Austria and Canada). Other separate sets with stronger social network features comprise Eastern European countries (set three) and Southern Europe countries (set four) in these sets, family contacts are above the international country average but community participation is less strong. Country sets with low comparative threshold scores in the QCA are argued to be likely to be in greater need of government care policy interventions.

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)0.015
metaresearch head score (Gemma)0.017
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 score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.010
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.117
GPT teacher head0.512
Teacher spread0.395 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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