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Record W7045038938

Age differences in volunteering experiences: an examination of generativity and meaning in life

2012· dissertation· en· W7045038938 on OpenAlexaboutno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2012
Typedissertation
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsGenerativityMeaning (existential)Association (psychology)Older peoplePurpose in lifeTest (biology)
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this thesis is to examine differences in volunteering experiences between middle-aged and older aged persons participating in the 2010 Vancouver Winter Olympics. Erik Erikson’s (1959/1994) concept of generativity is applied in order to test hypotheses pertaining to age-related associations between a pre-existing community volunteer role and meaning, self-esteem and meaning as well as sense of belonging and meaning. Data were utilized from the Older Olympic Volunteer Project which contained a dataset on aspects of volunteering experiences before and after an intensive and episodic volunteering event among 282 middle-aged and older adults. It was found that the association between a pre-existing community volunteer role and meaning in life was significant only for older adults, the association between self-esteem and meaning in life was discovered to be stronger for middle age adults, whereas the association between sense of belonging and meaning in life was found to be more robust among older adults. The results are discussed with respect to the concept of generativity and meaning in life.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
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 science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.260
Teacher spread0.230 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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