Evaluación emocional sobre el cambio climático en jóvenes andaluces: análisis de ecuaciones estructurales y multigrupo
Bibliographic record
Abstract
Climate change represents one of the most pressing environmental and societal challenges of our time. Psychoeducational research, since the mid-20th century, has identified a correlation between emotional processes and action-oriented decision-making. Emotional Appraisal Theory posits that emotions are the result of an evaluative process (both unconscious and/or conscious) that is conditioned by numerous factors. A comprehensive examination of the impact of certain factors and their modulation on emotional appraisal in the light of a climate emergency is considered a fundamental step in guiding educational interventions towards more holistic and climate action-oriented pedagogies.In this context, the present research aims to develop an exploratory structural model to understand the influence of the frequency of information consultation and perceptions of responsibility and risk on the emotional appraisals towards climate change. To this end, the structural equation modeling technique was employed in a sample of young individuals from eight provinces in Andalusia, Spain (n = 1.050). Furthermore, the potential impact of differing educational levels between participants on these relationships is investigated through the use of a multigroup moderation analysis.The level of adjustment of the proposed study model is favorable, with the capacity to explain almost half of the variance in negative emotions and close to a quarter of the variance in positive emotions. The results of the structural relationships study demonstrate the existence of notable causal patterns, with risk perceptions exerting a considerable influence on negative emotions towards climate change. Additionally, the substantial and incremental impact of social networks (both online and physical) and perceptions of externalizing causal responsibility for climate change, particularly as students progress through the educational system, represent crucial elements for educators to contemplate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".