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Record W4235023442 · doi:10.25124/idealog.v5i1.2806

THE EFFECT OF BUILDING QUALITY AND ENVIRONMENTAL CONDITIONS ON COMMUNITY PARTICIPATION IN MEDAN CITY HISTORICAL BUILDINGS

2020· article· en· W4235023442 on OpenAlexaff
Yuanita FD Sidabutar

Bibliographic record

VenueIdealog Ide dan Dialog Desain Indonesia · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsEnvironmental qualityTest (biology)Quality (philosophy)MathematicsSample (material)StatisticsGeographyEnvironmental sciencePolitical scienceGeology

Abstract

fetched live from OpenAlex

The potential for cultural tourism in Medan City in historical buildings. Objective 1) for to determine the quality of buildings on community participation in historical buildings in Medan City. 2) knowing the condition of the building's environmental area towards community participation in historical buildings in Medan City. 3) knowing the quality of the building and the condition of the environmental area on community participation in historical buildings in Medan City. The sample was 218 people. The data collection technique was distributed by questionnaires. Data was processed using Statistical Product Service and Solution version 20.0 for windows. Multiple linear regression data analysis techniques with the formula Y = a + b1X1 + b2X2. The result of multiple linear regression test is R2 of 0.637 or 36.3%, that there is an effect of building quality and environmental conditions on community participation in historical buildings in Medan City. With the results of the F test (Simultaneous) obtained Fcount of 35.53 and Ftable of 1.04, thus Fcount 35.53> Ftable 1.04 and it can be concluded that the quality of buildings and environmental conditions on community participation in historic buildings in Medan City have a significant effect.Keywords: Building Quality, Environmental Conditions, and Community Participation

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.339
Teacher spread0.302 · 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.

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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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