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Record W2884345497 · doi:10.17770/sie2018vol1.3190

EDUCATION AS AN AGENT OF RESOCIALIZATION OF ELDERLY PEOPLE

2018· article· en· W2884345497 on OpenAlexaboutno aff
Nataliia Kalashnyk, Yana Levchenko, Olha Doronina, Olha Kucherova, Olha Luchenko

Bibliographic record

VenueSOCIETY INTEGRATION EDUCATION Proceedings of the International Scientific Conference · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Issues in Poland
Canadian institutionsnot available
Fundersnot available
KeywordsResocializationFeelingElderly peopleActive ageingPopulation ageingGerontologyPsychologyPortraitPopulationSociologyMedicineSocial psychologyOlder peopleSocial scienceHistory

Abstract

fetched live from OpenAlex

Due to the aging of population there is a need for reevaluation of the importance and the necessity of elderly people’s participation in the life of society and as a result reevaluation of the methods of their adjustment. Modern “third aged people” want to continue living in the habitual rhythm of life, being the full participants of the society even after their retirement. The social portrait of a modern elderly differs significantly from the one 15-20 years ago. The level of medicine in the developed countries provides them with good state of health and therefore they can retain high level of social and emotional activity. Using several countries (Japan, Australia, Canada,) as an example the article aims to present different ways of inclusion of elderly people in socially useful activities in order to solve the problems of the modern society, in other words, to suggest how third aged people may be helpful. The article sums up that giving the opportunity of social and professional activity to the elderly people helps them to prevent social maladjustment and arising feeling of needlessness after the termination of active employment.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.362
Teacher spread0.336 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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Same venueSOCIETY INTEGRATION EDUCATION Proceedings of the International Scientific ConferenceSame topicSocial Issues in PolandFrench-language works237,207