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Record W3190849758 · doi:10.5539/jel.v10n5p38

The Local Wisdom Management Mohom for Stable Inherit and Lifelong Learning

2021· article· en· W3190849758 on OpenAlexvenueno aff
Nawatrot Intem, Thongchai Phuwanatwichit, Atchara Sarobol, Chetthapoom Wannapaisan

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningCitizen journalismSociologyContext (archaeology)Inheritance (genetic algorithm)Participatory action researchLocal communityKnowledge managementQualitative researchPublic relationsPedagogySocial sciencePolitical scienceGeographyComputer scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

This article aims to: (1) study the context of the local wisdom Mohom for self-reliance of Ban Thung Hong community, Phrae; and (2) to study the local wisdom management of Mohom for sustainable inheritance and lifelong learning, by implementing qualitative research methodology. Data collection was conducted by an in-depth interview on the community leader, the community philosopher, and Hom laborers, participatory and non-participatory observation, and collection of related secondary data. The results of the content analysis revealed that 1) The context of the local wisdom Mohom is related to participatory Hom management for self-reliance of the community in Phrae, via accumulation, transfer, and inheritance of knowledge from generation to generation. This is called “Hom Culture” which consists of self-reliance in three factors: (1) Hom Culture: community economy for self-reliance; (2) Hom Culture: social and cultural system for self-reliance; and (3) Hom Culture: natural resource and environmental management for self-reliance. Hom is therefore a culture that connects the community, and is significant for the community in Phrae to conserve the identity and value of the local wisdom, a foundation for self-reliance of the local community; and 2) Study on the local wisdom management of Mohom for sustainable inheritance and lifelong learning revealed that there are seven knowledge management processes in Ban Thung Hong community: 1) knowledge identification; 2) knowledge creation and acquisition; 3) knowledge organization; 4) knowledge codification and refinement; 5) knowledge access; 6) knowledge sharing; and 7) learning. Additionally, there also exists Hom cultural management in the community through the Mohom Dyeing Learning Center, in which community members design the communication process of Hom culture for participatory self-reliance in the community, and response to demands of the community via “Lifelong Learning” applied in the context of the community regarding learning among the members, or between members and teenagers, which eventually lead to the development of learning places, travel destinations, and local database as the learning sources of the community. Additionally, this allows those interested in the community, particularly the local young population, to study the community, which encourages “Strong Citizens of Phrae” via knowledge management, authority distribution, and educational opportunity creation, with the purposes to encourage equality and bonds in the community, alongside an appreciation of resources in one’s own community. One of the important expectations of the community is to preserve the lifestyle pattern of Hom culture, despite the social changes from external sources which influence the role of the community.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.313
Teacher spread0.299 · 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
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

Citations18
Published2021
Admission routes1
Has abstractyes

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