Natural Disaster Mitigation through Integrated Social Learning Science in Primary School
Bibliographic record
Abstract
This research aims to develop a learning model in disaster volcanic eruptions, floods and earthquakes integrated in social science subjects and in elementary school level. This learning model includes five features, namely: (1) the model syllabus and lesson plans, (2) the theme and subthemes, (3) teaching methods, (4) materials / textbooks and CDs about the disaster of nature, and (5) techniques and types assessment of student learning outcomes. Improving the knowledge and skills of teachers and students about the concepts, principles and practice self-rescue if the occurrence of natural disasters. This study is a research and development (R & D) in elementary school. This type of data consists of qualitative and quantitative data. Exploratory data analysis results based disaster mitigation model of learning is conducted qualitatively by descriptive percentage. Analysis of empirical test data using descriptive statistics percentages. Data were analyzed with the results of the implementation of parametric statistical tests, descriptive of the samples using a t-test. Research shows that learning device development results declared effective because it proved able to increase disaster mitigation skills of students, student learning, and the comfortable to be applied at primary school level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".