Bridging Policy and Grassroots Action With Technology: A Framework for Civic Engagement in Environmental Sustainability
Bibliographic record
Abstract
ABSTRACT Grassroots and civic organizations are increasingly recognized as “middle actors” in sustainability, leveraging ICT‐driven solutions to bridge top‐down policies with bottom‐up citizen engagement and behavioral change. This study examines how civic organizations in Finland and Singapore integrate digital tools to support sustainable waste management practices aligned with Sustainable Development Goals 11, 12, and 17. Guided by a Design Science Research (DSR) approach and drawing on Middle‐Out and multi‐level governance theories, we developed and validated the Integrated Sustainability Engagement Framework (ISEF) based on interviews with 21 civic organizations and iterative feedback. Findings highlight the central roles of policy translation, localized practices, digital engagement, behavioral reinforcement, and advocacy in civic‐led sustainability engagement. While civic organizations employ techno‐social tools to bridge regulatory gaps and promote actionable change, persistent economic, social, and regulatory constraints limit their reach. ICT‐enhanced engagement improves decision‐making and sustainability outcomes. ISEF provides a practical, theoretically grounded model for supporting multi‐level governance and accelerating sustainability transitions. The study recommends institutionalizing GCO roles through funding, digital infrastructure, and participatory mechanisms to strengthen civic capacity and create inclusive, adaptive pathways toward long‐term sustainability and impact.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".