The 9th Graders’ Relation of Futuristic Thinking Factors Study
Bibliographic record
Abstract
The objectives of this study are to study the relation of futuristic thinking and to create the prediction equation of the 9th graders’ relation of futuristic thinking. The subjects of this study are 860, 9th graders, studying in their second term in 2019 in Srisaket province, Thailand. The sujects of this study are from 12 schools from Multi-Stage Random Sampling. The study uses 216 students in especially big-size school, 160 students in big-size school, 302 students in medium-size school and 182 students in small-size school. The research instrument used are Futuristic Thinking form, Emotional Intelligence (EQ), Motivation (MO), Self-Directed Learning (SD) and Attitude towards learning (AT) with Likert Scale measurement (IOC), Item Total Correlation and Cronbach’s Alpha Coefficient. Statistics used are Mean, Standard Deviation, Pearson’s Product Moment Correlation (rxy), and Stepwise Multiple Regression Analysis. The study found that: All the five factors has relational statistics of .05 with Coefficient of Correlation from .673-.791 Prediction equation both in the form of unstandardized scores and standardized scores are FT¢ = .180 + .483EQ + .179MO + .172SD + .148AT ZFT¢ = .441ZEQ + .172ZMO + .179ZSD + .148ZAT All five prediction factors and variance factors can be used to explain 70.7% of futuristic thinking.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".