Climate Change Attitudes, Beliefs and Intentions Among Young Adults In an Institution of Higher Learning: Does Personality Matter?
Bibliographic record
Abstract
The present article is concerned with the relationships between personality traits and climate change attitudes, beliefs and intentions. This was done to determine the relationship that exists between personality traits and attitudes, beliefs and intentions towards climate change issues. A descriptive survey design was used in conducting this study. The sample comprised 203 undergraduate students (116 males and 87 females) selected from various Faculties in Obafemi Awolowo University, Ile-Ife, Nigeria. Convenience sampling technique was used to collect data from the respondents. Their age ranged from 15 to 35 years (M=23.6; SD=5.2). The Big Five Personality Inventory (BFPI) and the Climate Change Attitude Survey (CCAS) were used to collect data from participants. Results revealed that the vast majority of participants agree or strongly agree that human activities cause global climate change. (46% and 37.4% respectively). Furthermore, the results showed that there is a significant influence of personality dimensions on climate change attitudes, beliefs and intentions ({F (5,195) =20.327, p<.05, R²=.326}). Also, there is no significant difference between undergraduates in science faculties and non-science related faculties on attitudes, beliefs and intention towards climate change ({t (198) =-.827, p>. 05). The study concluded that personality traits are determinants of climate change attitudes, beliefs and intentions among undergraduates in the study area. The outcome of this study has implications for policy-making in the areas of capacity building and climate change education in institutions of higher learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".