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Record W2953303107 · doi:10.1002/ace.20330

Adult Education in China: Exploring the Lifelong Learning Experience of Older Adults in Beijing

2019· article· en· W2953303107 on OpenAlexaff
Shibao Guo, Wei Shan

Bibliographic record

VenueNew Directions for Adult and Continuing Education · 2019
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLifelong learningAdult educationBeijingChinaPsychologyAdult LearningNarrativeLife course approachSuccessful agingGerontologyGender studiesSociologyDevelopmental psychologyPedagogyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Abstract This research examines the role of adult and lifelong learning in facilitating successful aging among Chinese older adults. A life history research is adopted to conduct the study. By analyzing narratives of research participants’ learning stories, the study explores the provision of lifelong learning programs, Chinese perspectives on successful aging, and contributions of lifelong learning to successful aging and active citizenship among Chinese retired older adults. The findings show that the Chinese perspective on successful aging is closely connected to Chinese culture and traditions. Learning contributes to successful aging by bringing out older adults who are healthier and more independent, open‐minded, and socially active. The rich descriptions of historical and political events, Chinese traditions, moralities, and social values contribute to the understanding of Chinese older adults’ pursuit of lifelong learning and perspectives on successful aging. The detailed discussion of the motives and activities of older adults volunteering advances our extant knowledge about Chinese older adults’ volunteerism. China faces an aging population and adult and lifelong learning contributes to successful aging by bringing out older adults who are healthier and more socially active.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.334
Teacher spread0.321 · 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 teacher head, 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

Citations13
Published2019
Admission routes1
Has abstractyes

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