Fostering Systems Thinking for Youth Leading Environmental Change: A Multinational Exploration
Bibliographic record
Abstract
More youth-based environmental engagement programs (EEPs) are needed to help combat the impact of climate change. Current programs just focus their content on individual-level personal practices (e.g., recycling); are designed to be implemented in one setting with little regard for broader implications or opportunities for contextual adaption; and collect little evaluative information about how programs are developed, implemented, and evaluated. In this article, the authors present an example of how these limitations might be addressed through Youth Leading Environmental Change (YLEC), an evidence-based international EEP designed to build young people's capacity for collective action. The goal of this analysis is to explore one specific aspect of this program, fostering systems thinking, which is a critical element of the underlying theory of engagement and a critical skill in finding approaches to dealing with complex problems. Systems thinking is a form of analysis or thought process that places emphasis on how a problem interacts in complex ways with the systems in which it was created. We investigate how two novel program components, a local speaker's personal account of an environmental injustice and a live video exchange of participants from the global North and South (with different experiences of negative environmental impacts), promote systems thinking. We also illustrate how participants' increased capacity in systems thinking resulted in them being more motivated and engaged in collective environmental action. Thirty-four 60 minute semistructured qualitative interviews were conducted with participants in Bangladesh, Canada, and India. Findings suggest that participants' experiences of the two program components built their capacity to think about environmental issues at higher levels of systemic complexity, which, in turn, resulted in increased engagement in environmental action. Key Words: Environment—Youth engagement—Systems thinking—Adaptation—Behavior change.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".