Bibliographic record
Abstract
The purpose of this study was that verify the difference of pro-environmental behavior according to be present or not of experience environmental education for developing pro-environmental and show basic material to prepare efficient operation plans of experience environmental education is based on factor which effect of pro-environmental behavior difference according to the types. Elementary schools were divided through existence and nonexistence of an experience environmental education than I was checked up the pro-environmental behavior of students, who joined in different environmental educations each other. It was analysis by dividing into an environmental education of school, an educator (teacher, environmental-interpreter), and fields for environmental education (school inside and outside) to know types of experience environmental education. In result, elementary schoolers who experienced an experience environmental education at school class, had higher an environment-friendly behavior and elementary schoolers, who experienced an experience environmental education at class from environmental-interpreter, had higher an environment-friendly behavior by and large. As a result, this study showed that the area based on their residential quarter and an experience environmental education with realistic plans to connect with social environmental education for developing pro-environmental carrying out the most positive effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".