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Record W2800621562 · doi:10.1111/jomf.12469

Ambivalence in Later‐Life Family Networks: Beyond Intergenerational Dyads

2018· article· en· W2800621562 on OpenAlexaff
Myriam Girardin, Éric Widmer, Ingrid Arnet Connidis, Anna‐Maija Castrén, Rita Gouveia, Barbara Masotti

Bibliographic record

VenueJournal of Marriage and the Family · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsWestern University
FundersUniversité de GenèveSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsAmbivalencePsychologyDevelopmental psychologyGeneral partnershipFamily lifeLife course approachSocial supportSocial network (sociolinguistics)GerontologySocial psychologySociologyMedicineGender studiesPolitical science

Abstract

fetched live from OpenAlex

In later life, changing conditions related to health, partnership, and economic status may trigger not only support but also conflict and ambivalence, with the consequent renegotiation of family ties. The aim of this study is to investigate both conflict and emotional support in the family networks of older adults, taking the research beyond the level of intergenerational dyads. We used a subsample of 563 elders (aged 65 years and older) from the Swiss Vivre/Leben/Vivere survey. Multiple correspondence analysis and in‐depth case studies were used to identify the key social conditions that relate to the prevalence of conflicted and supportive dyads in family networks. Findings showed that the balance of conflict and emotional support in older adults' family networks varied according to the composition of their family network as well as their age, health, income, and gender.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0000.004
Research integrity0.0000.001
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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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 designObservational
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
Published2018
Admission routes1
Has abstractyes

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