Evaluating goodness-of-fit indicators for the construct validity and reliability of the scale of the "Dragons of Inaction" Psychological Barriers to climate change mitigation and adaptation: Studying differences using Bayesian probability
Bibliographic record
Abstract
This study aimed to culturally adapt the "Dragons of Inaction Psychological Barriers" scale developed by Lacroix and her colleagues (Lacroix et al., 2019) in the Canadian context, and measure the construct validity good-fit-indexes of the standard model consisting of five psychological barriers and their stability in the Arab environment. The study also sought to identify differences in the validity and stability indicators of the standard model for both male and female samples and differences in psychological barriers to climate change mitigation and adaptation efforts due to education level, age, and social status. The sample consisted of (497) adults in Saudi Arabia, aged between (27– 55) years (37.96 ± 7.88 years), with (279) males and (218) females. The "Dragons of Inaction Psychological Barriers (DIPB)" scale was applied to the study sample, developed by Lacroix et al. (2019) and adapted by the researcher for the Arab context in this study. The results indicated that the scale has good validity and stability indicators for both dimensions and the overall scale. Confirmatory factor analysis revealed that the standard five-factor model of psychological barriers had good of-fit construct validity indexes, with no differences between male and female standard models in terms of validity and stability indexes, as both showed good validity and stability. Additionally, the Bayesian independent samples t-test showed differences in psychological barriers to climate change mitigation and adaptation efforts due to education level, age group, and social status. Based on these findings, the study recommended the urgent need to measure the psychological barriers to interpret the gap between individuals' attitudes towards climate change and the actions they take to mitigate it, to develop plans and policies based on scientific evidence. It also recommended the importance of designing programs to reduce these psychological barriers among individuals.
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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 | 0.051 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".