Analysis of students' environmental literacy at senior high school 10 palembang on environmental pollution material
Bibliographic record
Abstract
The global concern for environmental issues is widely discussed worldwide. Environmental literacy, encompassing concerns for the environment, is crucial to prevent human-induced environmental damage. Education plays a pivotal role in enhancing this literacy. This research aims to describe the environmental literacy abilities of students at Senior High School 10 Palembang, namely a school that is environmentally conscious regarding environmental pollution material. The research method is descriptive research and survey techniques. The research sample was 36 students of class XI Science. The sample used a purposive sampling technique. The research instrument includes 31 multiple-choice questions for knowledge and cognitive skills and 37 questionnaire statements for behavioral and affective indicators. Data analysis was carried out by calculating the average achievement of indicator students' environmental literacy scores in the form of percentages. Analysis revealed an overall environmental literacy percentage of 49.06% in the quite good category. However, specific indicators, notably knowledge (39.44%) and cognitive skills (29.52%) poor category. Behavioral indicators (60.94%) were quite a good category, and affective indicators stood (66.33%) good category, indicating room for improvement, especially in knowledge and cognitive skills. Teachers could enhance environmental literacy by employing innovative strategies, particularly in biology classes discussing environmental pollution.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".