The Development of Environmental Education Learning on Sustainable Participation of Lamphun Sub-district, Muang District, Kalasin Province, Thailand
Bibliographic record
Abstract
The objectives of this research were to 1) develop a participatory environmental education learning activity manual for sustainable environmental management for communities in Lampan sub-district, Muang district, Kalasin province (SDMDKOP); 2) study knowledge, attitudes, and participation on sustainable participative environmental management of household heads in SDMDKOP; and 3) compare levels of knowledge, attitude, and participation in the sustainable participatory environmental management of household heads in SDMDKOP with different occupations and incomes. The sample group in this research consisted of 64 heads of households in SDMDKOP communities who voluntarily participated. The research instruments were a participatory environmental education learning activity manual for sustainable environmental management for the community, a knowledge quiz, an attitude scale, and a participation measure. The statistics used in the data analysis were percentage, mean, and standard deviation. The hypothesis test statistic, t-test, F-test (one-way ANOVA), and the comparison of the mean scores by pairs by the Scheffe method. The research found the following: 1. A handbook of participatory environmental education learning activities for sustainable environmental management for SDMDKOP reported that the efficiency was 94.02/92.30, which was higher than the set threshold, and the learning effectiveness index was 0.7189 or 71.89 percent. 2. After the activity, the heads of households in SDMDKOP had higher knowledge, attitude, and participation level than before the activity. 3. Before the activity, for the heads of households in SDMDKOP with different occupations and incomes, the average level of knowledge per person was 29.04, and after the activity, the level was 36.92. People with different occupations had different attitude levels before organizing recreation area management activities (p <. 05). The contracting profession has different attitude levels for agriculture and government officials. As for the hired profession, the attitude level was different from government officials. After the activity, there was no difference in attitude level. The level of participation before and after the activities did not differ. Those with different incomes had no difference in attitude and level of participation before and after the activities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".