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Record W7116928935 · doi:10.5539/jel.v15n2p386

Development of a Zero-Waste Management Model in Educational Institutions under Khon Kaen Municipality

2025· article· W7116928935 on OpenAlexvenueno aff
Ekkarach Kositpimanvech, Jiraporn Phansawang, Chayanit Kositpimanvech

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Language
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
FundersThailand Science Research and Innovation
KeywordsSustainabilityFocus groupReuseManagement developmentData collectionHigher educationNeeds assessment

Abstract

fetched live from OpenAlex

This study aimed to develop and evaluate a zero-waste management model for educational institutions under Khon Kaen Municipality. The research was conducted in three phases. The first phase investigated the current situation, desired condition, and essential needs related to zero-waste management using a needs assessment survey administered to 242 administrators and teachers. Results revealed a clear gap between current and desired practices, with the highest development needs identified in the areas of Reuse and Avoid. In the second phase, a zero-waste management model was designed through expert interviews and focus group discussions. The model was then validated by five experts and rated at the highest level for appropriateness, usefulness, and feasibility. In the third phase, the model was implemented on a trial basis and evaluated by 24 stakeholders, who also rated its practicality and suitability at the highest level. The findings highlight the necessity of a structured approach to zero-waste management in schools and demonstrate that the developed model is both contextually appropriate and operationally feasible. The study contributes a practical framework that can inform educational policy and promote environmental sustainability within the school system. Recommendations are provided for future research to explore broader applications and for educational stakeholders to integrate the model into institutional practice.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.334
Teacher spread0.291 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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