Promoting E-Governance Through Capacity Development for the Global Environment
Bibliographic record
Abstract
Solving the world’s great crises and taking advantage of the world’s greatest opportunities requires innovation and capacity. E-governance uses Information & Communication Technologies (ICT) to broaden participation and make problem solving more effective. Environmental issues represent some of the world’s greatest crises and most significant opportunities. The United Nations Environment Programme (UNEP) is a global leader in tackling such issues. UNEP’s Global Environmental Outlook (GEO) relies on contributions from many experts from all regions of the world. Because capacity development is critical to UNEP’s ability to fulfill its mandate, UNEP collaborated with the International Institute for Sustainable Development (IISD) to commission more than 50 experts to develop training resources for integrated environmental assessment and reporting at the sub-global level. These resources were adapted to an eLearning format, significantly broadening their utility and reach. All materials are organized onto the Moodle Learning Management System and use a facilitated interaction model. The eLearning curriculum and approach has been peer reviewed and pilot tested. This research has further developed a blended learning, Train-the-Multipliers program to train facilitators. This eLearning implementation has clearly demonstrated potential and is advancing e-governance at global, regional, national and sub-national levels in the area of environmental assessment and reporting. Although UNEP’s position as a strong proponent of global environmental governance is unique, the detailed approach described for the eLearning programme is generic and therefore, would be a useful model for others who wish to develop eLearning curricula.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".