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Record W3157548377 · doi:10.17722/ijme.v12i1.1047

The Elderly Educators in Their Quest for Lifelong Learning: Some Stories to Tell

2018· article· en· W3157548377 on OpenAlexvenueno aff
Maylin Marabe Blancia, Minerva T Fabros, Norman o Blancia

Bibliographic record

VenueInternational Journal of Management Excellence · 2018
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningThematic analysisPedagogyPsychologyMindsetProfessional developmentPromotion (chess)AndragogyCoachingMedical educationAdult educationSociologyQualitative researchMedicinePolitical science

Abstract

fetched live from OpenAlex

This phenomenological study explored the lived experiences of 20 elderly teachers, 50 to 65 years old, still in the service and enrolled in post graduate education. Through in-depth interviews of 10 informants and focused group discussion of 10 participants, the data were gathered and subjected to thematic analysis. The results revealed that the elderly teachers decided to enroll in post graduate education for reasons of dealing with age-specific issues and expectations, balancing personal and professional responsibilities, being infused with young brood and new perspectives, aspiring for career advancement opportunities, enhancing professional portfolio, and rearranging needs and priorities were the themes. The themes for the challenges of the elderly educators connected to their quest for lifelong learning included, balancing and prioritizing, keeping abreast with technology and new sources of knowledge, being optimistic and positive, and being determined and resolute. The insights of lifelong learning shared by informants and participants were, professional advancement brings promotion opportunities, lifelong learning is personal development, age does not matter in learning, and lifelong learning has its rewards. What is notable in this study is the participants’ being still active and generative, their resiliency to face the challenges, and their insights of wisdom, hope, and faith.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.033
GPT teacher head0.391
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2018
Admission routes1
Has abstractyes

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