Penanaman Pendidikan Karakter Melalui Pengelolaan Bank Sampah di Lingkungan Sekolah
Bibliographic record
Abstract
This research reveals the importance of instilling character education to every learner at every level through the Waste Bank. Waste Bank is one of the media in instilling two of the eighteen character education values of the Ministry of Education and Culture, namely creative and environmental care. This research uses a qualitative approach with a descriptive research type. Data were obtained through literature studies collected by tracing articles, reading literature, and analyzing research results. The results showed that the presence of Waste Bank in schools is expected to provide fresh air in the world of education, especially in instilling environmental and entrepreneurial character education in students. It is hoped that the Waste Bank will contribute to the independence of students to manage waste in their community environment.Planting entrepreneurial character from an early age is also very important so that students have a mentality of job creators instead of job seekers. Therefore, the main challenge is for authorities and stakeholders to coordinate and facilitate educational institutions to create a sustainability-based education system to create creative and environmentally-conscious outputs of prospective entrepreneurs given the mountains of waste in various parts of Indonesia.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".