Bibliographic record
Abstract
Effective waste management in educational institutions is crucial for fostering sustainable practices. Higher Education Institutions are recognized as mini cities with significant daily populations, necessitating comprehensive waste management strategies. These institutions play a pivotal role in sustainability. A systematic review of waste management in HEIs suggests that the most effective practices include the engagement of the academic community through teaching and research. These efforts are complemented by the identification of sustainability indicators that assist in decision-making and the continuous improvement of campus sustainability. The integration of environmental management systems can further enhance these initiatives, as demonstrated by case studies in universities like TERI University in New Delhi, which showcase the benefits of such systems in educational settings. Overall, the development of sustainable waste management in educational institutes is not only about compliance with environmental regulations but also about building a culture of sustainability that resonates with all stakeholders involved. This paper will work to define this through literature review, then mention the proposal which applied in university of Canada-New capital-Egypt, finally Making an international online survey for universities users to gain the results which lead to know the knowledge of this categories of users about waste management and the necessary of Appling it. On the other hand, the results of several studies shows that at least 87% of the waste generated on building may be recycled. To ensure sustainable solid waste management and reliable recycling program, a university must run fixed program to be operated.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".