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Record W2591808109 · doi:10.5539/ies.v10n3p194

Profile of Knowledge Management, Basic Sanitation and Attitudes towards Clean and Health Community in Kupang City

2017· article· en· W2591808109 on OpenAlexvenueno aff
Nikmah Nikmah, Muhammad Ardi, Mohamad Hasyim Yahya, Muhamad D. Pua Upa, Gufran Darma Dirawan

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Administration in Developing Nations
Canadian institutionsnot available
Fundersnot available
KeywordsSanitationDescriptive statisticsClean waterBathingEnvironmental healthHand washingPsychologyHygieneSocioeconomicsMedicineEngineeringSociologyMathematicsEnvironmental engineeringWaste managementStatistics

Abstract

fetched live from OpenAlex

The objective of research is to describe the knowledge and attitude of basic sanitation management community in Kupang City. This type of research is a survey research using quantitative approach. Data were collected by using the instrument in the form of test knowledge of basic sanitation management and attitude questionnaire. The data was then processed and analyzed using descriptive statistics. The Result of the research was sanitation management knowledge base is in low category whereas attitude clean and healthy life, about 61.5% of Kupang not leave a comment. The knowledge is still low is illustrated by the habit of living of the community, particularly in watersheds where there are many people using the river as a final disposal of feces them also as a public bathing and washing.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

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 studies0.0010.001
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 head0.283
GPT teacher head0.548
Teacher spread0.265 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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