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Record W2012649899 · doi:10.2224/sbp.2005.33.5.477

AGE, CULTURE, AND THE ANTECEDENTS OF LONELINESS

2005· article· en· W2012649899 on OpenAlexaboutno aff
Ami Rokach, Félix Neto

Bibliographic record

VenueSocial Behavior and Personality An International Journal · 2005
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessPsychologySituational ethicsPersonalityRelocationPortugueseDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Loneliness is a pervasive experience which everyone has experienced. It is a subjective experience, which is influenced by one's personality and situational variables. This study examined the influence of age and culture on the perceived causes of loneliness. One thousand, three hundred and forty-seven Canadian and Portuguese participants from all walks of life volunteered to answer an 82-item yes/no questionnaire reflecting on the causes of their loneliness. The questionnaire used in this study is composed of the factors that describe causes of loneliness: Personal inadequacies, Developmental deficits, Unfulfilling intimate relationships, Relocation/significant separations, and Social marginality. Gender differences between and within the groups were also examined. Four age groups were compared: youth (13–18 years old), young adults (19–30), adults (31–58) and the elderly (60 and older). Within and between culture and age comparisons were also done. Results indicated that the causes of loneliness are perceived differently depending on one's age and culture.

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)0.004
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 score0.034
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.059
GPT teacher head0.418
Teacher spread0.359 · 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

Citations86
Published2005
Admission routes1
Has abstractyes

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