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Record W4200612617 · doi:10.18280/ijsdp.160714

Traditional Climate and Environment Forecasting Based on Local Knowledge of Urug Societies in Bogor West Java

2021· article· en· W4200612617 on OpenAlexvenueno aff
Bahagia, Fachruddin Majeri Mangunjaya, Endin Mujahidin, Rimun Wibowo

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCultural and Artistic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyIndigenousSociologyTraditional knowledgeNonprobability samplingLocal communityPsychological resilienceResilience (materials science)Climate changeCommunity resilienceJavaGeographySocial scienceEnvironmental resource managementEcologyAnthropologyComputer scienceSocial psychologyPsychologyEnvironmental sciencePopulation

Abstract

fetched live from OpenAlex

The aim of this research to find out about Indigenous of Knowledge Urug Community for forecasting climate and environment dynamic toward community resilience. The research method used is the ethnographic approach or cultural anthropology. Ethnography is sorts of qualitative research that need observation, documentary and interview in local societies. Ethnographic deal with discovers about description about culture including local knowledge, behaviour, cultural, ritual, traditional ceremonies, and language of Urug community. The selection of sample as informant exert purposive sampling technique. The result is probed meticulously through triangulation technique and triangulation sources. The result shows that the Indigenous community have implemented the sort of Traditional and calendar years with animals symbol for forecasting season and climate. The community can adapt season and climate dynamic and create community resilience for environment and climate. Besides that, there is the connection of planting local paddy to attaint community resilience including environmental change, cultural and social resilience.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.207

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 head0.051
GPT teacher head0.279
Teacher spread0.228 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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