ICIMOD strategy 2030: Moving mountains
Bibliographic record
Abstract
This is a pivotal moment in our history. The world around us is changing and here, on the top of the world, things are changing fast. We are witnessing rapid climate change, biodiversity loss, increased disaster risk, and rising poverty and inequality. This pre-formatted version of the strategy for 2023-2030 outlines how we will work to address these challenges and achieve our vision of a greener, more inclusive, and climate resilient Hindu Kush Himalaya. It has been developed in response to a request of our Board of Governors and International Support Group to raise our ambition, with ICIMOD@40 in 2023, and in the face of the climate and environment crises hitting the region. It draws on our learning from the last two plan periods, quinquennial reviews, and a broad-based strategy development process involving staff, donors, partners, and nodal agencies in the Regional Member Countries (RMCs). It describes our institutional theory of change and the impact areas and pathways that will guide our work to effect that change. It highlights the partnerships that will enable this change – with our eight RMCs; government, civil society, and private sector partners; and our donors and supporters across the world. The strategy envisages a new and effective monitoring, evaluation and learning system in place to enable continuous learning, strategizing, and course correction, with an emphasis on learning from both success and failure. Lastly, it charts out the resource mobilisation goals that will enable climate action at scale in the RMCs.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.025 |
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".