The role of sustainability knowledge-action platforms in advancing multi-stakeholder engagement on sustainability
Bibliographic record
Abstract
Abstract Within the last decade, online sustainability knowledge-action platforms have proliferated. We surveyed 198 sustainability-oriented sites and conducted a review of 41 knowledge-action platforms, which we define as digital tools that advance sustainability through organized activities and knowledge dissemination. We analyzed platform structure and functionality through a systematic coding process based on key issues identified in three bodies of literature: (a) the emergence of digital platforms, (b) the localization of the sustainable development goals (SDGs), and (c) the importance of multi-level governance to sustainability action. While online collaborative tools offer an array of resources, our analysis indicates that they struggle to provide context-sensitivity and higher-level analysis of the trade-offs and synergies between sustainability actions. SDG localization adds another layer of complexity where multi-level governance, actor, and institutional priorities may generate tensions as well as opportunities for intra- and cross-sectoral alignment. On the basis of our analysis, we advocate for the development of integrative open-source and dynamic global online data management tools that would enable the monitoring of progress and facilitate peer-to-peer exchange of ideas and experience among local government, community, and business stakeholders. We argue that by showcasing and exemplifying local actions, an integrative platform that leverages existing content from multiple extant platforms through effective data interoperability can provide additional functionality and significantly empower local actors to accelerate local to global actions, while also complex system 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 | 0.061 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 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".