Multi-Sectoral Partnership for Waste Management Evaluation and Awards Recognition in Higher Education
Bibliographic record
Abstract
Waste management is an important part to achieve green and sustainable campus. This study aims to evaluate waste management implementation in higher education. The methodology used in this study is a cross sectional with a non-probabilistic sampling. Data were collected using a well-structured evaluation instrument through an online focus group discussion, document review and evidence of implementation. The evaluation instrument consists of 10 elements: waste management policy, resource availability, waste segregation, waste collection, temporary waste storage, handling of general and hazardous waste, personal protective equipment (PPE), waste segregation awareness educational program, and evaluation on waste management. There were 15 faculties/schools/program were participated. Data was analysed using univariate analysis, radar plot representation, Box and Whiskers plot analysis. The level of waste management implementation amongst faculties /schools/program was varied between 52% to 98%. Higher education needs to evaluate waste management implementation and established a systematic environmental awareness program to achieve sustainability development goals (SDGs). The mean score ranking from highest to lowest level: personal protective equipment (5.6) to evaluation of waste management implementation (3.2). Indeed, to ensure a comprehensive general waste management, it was suggested that higher education need to build a centralized waste collection facility, a chemical waste treatment and competence personnel for handling laboratory waste.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.109 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".