Deviation of Management of Medical Waste from Hospital Implication upon Environmental Damage in West Sumatera, Indonesia
Bibliographic record
Abstract
The purpose of this study is to show and analyze suspected abuse of power by hospital administrators in North Sumatra with public officials, in this case the bureaucrats in the Ministry of Environment of West Sumatra Region, carrying out hazardous medical waste out of place according to environmental science regulations, so that it occurs environmental damage. This research approach method is a qualitative method. Creswell defines a qualitative method as a research method that is based on the perspective of constructivism, namely various meanings, meanings that are socially and historically constructed with a view to developing a theory or pattern. Researchers collect data that is open-ended and developing, with the aim of developing a theme from the data obtained. The data collection technique is carried out by literature study of primary and secondary and tertiary legal materials. Environmental regulation analysis techniques, with analysis using the theory of abuse of power. So that researchers draw conclusions deductively, and provide recommendations or suggestions according to research objectives The results found here were that the actors of the management of hospital medical waste disposal were not in accordance with hospital regulations and environmental regulations. Suggestions are provided by researchers for environmental crime prevention in an effort to prevent and tackle environmental destruction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".