The Youth Leading Environmental Change Project: A Mixed-Method Longitudinal Study across Six Countries
Bibliographic record
Abstract
Significant cultural transformations of the kinds that are needed to move our global society toward sustainability require youth to engage in environmental actions. These actions are more than just updating one's personal practice (e.g., recycling). They are “intentional and conscious civic behaviors that are focused on systemic causes of environmental problems and the promotion of environmental sustainability through collective efforts” (Alisat & Riemer, 2015, p. 14). The current study investigated the effectiveness of the Youth Leading Environmental Change (YLEC) program, which fostered such environmental actions in six participating countries. YLEC is an 11-unit evidence-based youth engagement workshop series, with a focus on environmental justice and on building action competence. The study employed a mixed-method longitudinal comparison group design with three follow-ups at 3, 6, and 12 months. Overall, 365 university students from Bangladesh, Canada, Germany, India, Uganda, and the United States participated in either the workshop or comparison group. Sixty-three of the workshop participants participated in semistructured qualitative interviews at the 3-month follow-up. The results suggest that most participants experienced a significant personal transformation both in regard to how they relate to environmental issues and how they perceive themselves as agents of change. Although there was an increase in environmental action in the month immediately following the workshop series, engagement seemed to revert close to baseline levels at the 12-month follow-up for many participants. Implications of the findings for theory and practice are discussed. Key Words: Youth—Environmental action—Environmentalism—Youth engagement—Environmental activism.
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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 | 0.016 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".