Engaging Youth for Sustainable Development:Field Lessons from Community Sustainability Global
Bibliographic record
Abstract
Abundant empirical evidence and studies tell us youth (age 15-24) have some potential to facilitate sustainable development (SD) policy and action, including awareness and activism, agenda setting, formulation, legitimation, decision-making and implementation. However, we know more about their activism and implementation role in the global north than south, especially Africa. Also, of the three basic pillars — economic, social, and environmental — of SD, youth are often reported and visibly seen to be more active on the environmental front, especially SD goal 13: climate action. While they could also foster economic and social sustainability, we know too little about that potential. To contribute to filling these gaps, we ask what youth could do to enhance the three pillars of SD across the global north and south. We use an international project, Community Sustainability Global (CSG), as a case study to answer the question, drawing on workshop preparation and interaction, feedback surveys and interviews. Among other findings, our results show that youth contribute not only to promoting SD policy and action but could also tell us more about knowledge mobilization on scientific and policy loopholes.
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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 | 0.032 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".