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Record W2276388727 · doi:10.1177/0190272516628297

The Age-Graded Nature of Advice

2016· article· en· W2276388727 on OpenAlexaff
Markus H. Schafer, Laura Upenieks

Bibliographic record

VenueSocial Psychology Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLife course approachMeaning (existential)PsychologyAdvice (programming)ScholarshipSocial psychologyOddsPerspective (graphical)Developmental psychologyLogistic regressionMedicine

Abstract

fetched live from OpenAlex

Drawing from life course, social networks, and developmental social psychology scholarship, this article considers how advice transmission varies across age groups and examines the age-contingent associations between advice-giving and life meaning. Binomial and ordered logistic regression using the 2006 Portraits of American Life Study ( n = 2,583) reveal that adults in their twenties are most likely to report offering advice to multiple social targets. Notably, however, the connection between advice-giving and life meaning is most pronounced for late-middle age adults—even as changes during this part of the life course reduce the odds of advice exchange. Consistent with developmental theory and the mattering perspective, we argue that advice is a mechanism for contributing to others’ welfare and for cultivating life meaning. Yet opportunity structures for advice transmission also shift over life course, leaving adults in late-middle age and beyond with fewer opportunities to engage in such generative practices.

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)0.001
metaresearch head score (Gemma)0.020
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 score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.342
Teacher spread0.330 · 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

Citations46
Published2016
Admission routes1
Has abstractyes

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