Waste Management Mind Map: Public Environmental Awareness Project and Creativity in Knowledge and Performance
Bibliographic record
Abstract
A progressive society wants to raise awareness, especially what affects everyday life. This study focuses on simplification waste management by using Mind Map. The cornerstone of this research is the use of mind maps in the field of environmental awareness, increasing environmental awareness of waste negative impact, minimizing its use, and the healthy and environmentally safe alternative. The researcher seeks to link the concepts and goals the 17 goals of sustainable development of United Nations Educational, Scientific and Cultural Organization (UNESCO), with awareness of waste management and modern theories of learning, in order to reach new methodology used in awareness activities. Applied the feasibility and impact of Mind Maps on understanding and simplifying the waste management course for students’ community at the Environmental Health department _ College of Health Science. Buzan’s iMindMap 4 software was used to create mind maps for simplifying the project of waste management, the software uses lines, colours, arrows, branches to show connections between the ideas generated. Results of meta-analysis of concept waste management mind maps in student community indicated that concept mapping has positive effects on understanding, assimilation and application, Post-test results showed that experimental community made higher gains in understanding, easy to add ideas, Help you focus on the links and relationships between ideas so you don't just have disconnected facts.Researchers will be based on results in the awareness activity to reach an optimal understanding of the negatives of waste and the responsibility of the community.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".