Exploring Children's Emotional Responses to Pollution: Implications for Environmental Education
Bibliographic record
Abstract
Understanding young children's emotional responses to environmental issues is crucial for shaping their attitudes and behaviours towards the environment. In the current study, we explore these responses through the lens of Social Learning Theory. Employing a constructivist paradigm, the research examines how children's interactions and experiences shape their perceptions and emotional reactions to environmental challenges. Using an interpretive research design, the study focuses on three children aged 4 to 5 years, selected from diverse preschools in Bloemfontein, to capture a broad spectrum of emotional responses to environmental stimuli. Two distinct images; one depicting dead fish surrounded by garbage and the other showing protesters burning tires—were used to provoke emotional reactions and reflections on environmental pollution and its consequences. Semi-structured interviews were conducted to gain insights into the children’s feelings, thoughts, and interpretations of these scenarios. Findings highlight the role of observational learning and social context in shaping children’s environmental attitudes and emotional responses. The study underscores the importance of understanding young learners’ perspectives on environmental issues, revealing the intricate relationship between human activities and ecological health. This research contributes to the broader discourse on environmental education and the emotional dimensions of early childhood 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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".