Model of Psychological State Affecting to Global Warming Alleviation
Bibliographic record
Abstract
The intention of psychologists tried to understand on human behavior, and then they had developed a large number of theories and models but they had the main focus on explanation how individual perceived and evaluated the stimulant before making decision to express his behavior. However, study on human behavior, it can’t be ignored the psychological state. Psychological state is a mental condition in which the qualities of a state are relatively constant even though the state itself may be dynamic but it contains certain characteristics that might be permanent for period of life. Especially, people is inspired for value of self-living, value of family living, attitude of sufficiency, religion belief, and Environmental Physical. The populations was 35, 010 undergraduate students of the first semester of academic year 2011 of Mahasarakham University. The simple random sampling was used to collect the sample for 450 undergraduate students with equivalent proportion according to fields of study. The questionnaire was employed as instrument for data collecting. LISREL was used for model verification. Results illustrated that the structural model, confirmatory factors of Psychological State (STATE) were able to explain the variation of confirmatory factors of Inspiration of Public Consciousness to caused Environmental Behaviors for Global Warming Alleviation with 63.4 percents. Therefore, the equation can be written as following. BEH = 0.38 MIND + 0.29 STATE (1) (R2 =0.57) Key words: Model; Psychological State; Affecting; Global Warming Alleviation
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".