Defining Youth Environmental Value Towards First Class Mindset Component: A Scale Development
Bibliographic record
Abstract
One of the elements to creating a first-class mindset among youth is environmental value. The present study was conducted to validate the component to build the model of environmental values amongst Malaysian youths with first-class minds. One thousand Malaysian youths were selected from five geographical zones (North, Central, South, East Coast, and East Malaysia). The sample was chosen using a stratified random sampling technique that considered gender, age, and location. A questionnaire was used as the research instrument. The environmental value constructs generated via exploratory factor analysis (EFA) were anthropocentric, ecocentric, and egocentric. The data were analysed to determine reliability using Cronbach’s alpha. Confirmatory factor analysis (CFA) was used to obtain a three-factor solution with SPSS Version 22 and AMOS Version 20. The analysis showed that Cronbach’s alpha was greater than 0.7, indicating high reliability. According to the recommended fit indices, the CFA analysis for the measurement model revealed that the three-factor solution was acceptable (CMIN=58.739, DF=32, CMIN/DF=3.967, GFI=.905, CFI=.966, TLI=.965, and RMSEA=.092). Therefore, the 28-item measurement model developed was appropriate for assessing the level of environmental value amongst youths with first-class minds. It could be used as a backup when developing environmental value instruments.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".