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Record W2962757019 · doi:10.5539/ass.v15n8p38

Education Activities to Realize Green Campus

2019· article· en· W2962757019 on OpenAlexvenueno aff
Hilma Tamiami Fachrudin, Khaira Amalia Fachrudin, Wahyu Utami

Bibliographic record

VenueAsian Social Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
Fundersnot available
KeywordsRatificationSustainabilitySustainable developmentPublic relationsUniversity campusBusinessMedical educationEngineeringPolitical scienceArchitectural engineeringPolitics

Abstract

fetched live from OpenAlex

The application of green concepts on campus starts from the formulation of the vision and mission, ratification of policies and development processes. The main problems in the effort to realize a green campus include a lack of involvement and individual awareness of sustainability issues and campus users are unaware of the sustainability that occurs on campus. Individual awareness and involvement of staff and students are needed to create a green campus. The purpose of this study was to analyze what educational activities should be carried out to increase awareness of realizing a green campus. There are several educational activities regarding green concepts aimed to campus users. Education activities such as courses on sustainable environment, research, seminars, student organizations in environmental activities, green campus promotions/campaign and paperless became independent variables in this study. This study uses quantitative methods with Factor analysis. The research was conducted at the Universitas Sumatera Utara. Data was collected by distributing questionnaire forms with respondents as many as 400 students. The results showed that education, practical and seminar activities about the importance of green concepts, green campus campaigns, and green applications on campus such as paperless needed to be done in raising awareness to realize a green campus.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.521
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.359
Teacher spread0.349 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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