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Record W2025781034 · doi:10.1080/15427609.2014.967050

Age Relations and Family Ties Over the Life Course: Spanning the Macro–Micro Divide

2014· article· en· W2025781034 on OpenAlexaff
Ingrid Arnet Connidis

Bibliographic record

VenueResearch in Human Development · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsWestern University
Fundersnot available
KeywordsLife course approachMacroPsychologyStrong tiesCourse (navigation)Family tiesInterpersonal tiesDevelopmental psychologySocial psychologyComputer scienceHistoryGenealogyPhysics

Abstract

fetched live from OpenAlex

A common divide between macro- and microlevel analyses of family ties and aging separates what happens inside and outside families over the life course. This macro–micro divide, its shortcomings, and possible resolutions are discussed in the context of the recession and economic downturn that began in 2007. Conceptual frameworks that facilitate a multilevel analysis are explored and applied to macrolevel structured social relations, mesolevel social institutions, and microlevel interpersonal relations. The impact of and response to the recession occurs at all three levels; and yet, there is often a disconnect among the recession’s repercussions, public and private-sector responses to it, and the efforts made within families to negotiate new circumstances. The value of spanning the macro–micro divide to complement individual adaptation with supportive policy responses in government and the workplace is discussed.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0000.002
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 head0.078
GPT teacher head0.394
Teacher spread0.315 · 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 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

Citations23
Published2014
Admission routes1
Has abstractyes

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