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Record W2785877995 · doi:10.5539/ass.v14n2p12

The Unsustainability of Kalego Traditional Game among Muna Community of Watopute District

2018· article· en· W2785877995 on OpenAlexvenueno aff
La Ode Ali Basri, Abdul Halim Momo, Akhmad Marhadi, Abdul Rahman, La Ode Topo Jers, Aslim Aslim, Aswati Aswati

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsStatisticInheritance (genetic algorithm)Participant observationAttendanceSocioeconomicsSociologyGeographySocial scienceEconomic growthStatisticsEconomicsMathematicsBiology

Abstract

fetched live from OpenAlex

This study aims at analyzing the unsustainability causes of kalego as one of traditional games of Muna communities in Watopute District, Muna Regency, Southeast Sulawesi. Data collection was conducted through participant observation, interviews, surveys, and focused discussion. Data analysis was conducted qualitatively and descriptive statistic by employing data reduction technique, data presentation and conclusion. The results showed that kalego traditional game has been degraded or unsustainably practiced as the impact of the low awareness and low understanding among Muna community toward this traditional game. Approximately 71.25% Muna community in Watopute district cannot play the traditional game. The unsustainability of kalego was caused by several factors namely; 1) the absence of the family inheritance about kalego traditional game and the decreasing number of old figures who understand Muna culture contribute to the decreasing of local culture knowledge among younger generations; 2) Cultural encounters which results incorporated acculturation toward Muna's local culture; 3) the absence of the massive kalego game staging.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.033
GPT teacher head0.321
Teacher spread0.287 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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