Addressing the Multidimensional Impacts of Climate Change through Effective Capacity Development, Research Leadership, Strategic Networking, and Partnerships – The case of the One Planet Fellowship
Bibliographic record
Abstract
In September 2019, a partnership consortium[1] joined efforts to launch the One Planet Fellowship. This initiative, announced at the inaugural One Planet Summit in Paris, December 2017, was the coalition on the mobilization of researchers and young people to work for the climate. The One Planet Fellowship, a career development program, aims to build a robust pipeline of scientists equipped to lead climate change research in Africa, establishing an intergenerational network of scientists across Africa and Europe to foster research collaborations and bringing gender equality at the centre of the climate change and agrifood systems research.Building on the summary points on adaptation from the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report chapter on Africa, it was evident that applied research is essential to solve the challenges of adaptation of agriculture and food systems in Africa. Many of the basic underpinning aspects of the required research are available – what is now needed is the in-depth analysis, and deployment of resulting strategies and practice across the African continent. This requires a strong commitment of African scientists at different levels of career development and collaboration with non-African researchers, particularly from Europe as its nearest continental neighbour through its high level educational and research institutions.It is thus important to invest in building the capacity of the next generation of African scientists so that they can be well-connected to the world as they find science-based solutions that are anchored in their local realities. To achieve this, the One Planet Fellowship targeted emerging, mid-career and seasoned scientists working in Africa and Europe to accelerate the career development process by:Strengthening the leadership, scientific and mentoring skills of emerging agriculture-climate scientists from both continents and providing an opportunity to African scientists to share their context-specific knowledge and mentor emerging scientists from Europe and for European scientists to gain valuable exposure to the context within which scientific research is conducted on the African continent. Incentivizing intra-continental research collaborations through the establishment of diverse research groups where Laureates identify, initiate, and implement joint projects of various forms including joint publishing, resource mobilization, and media engagement among others. Enhancing the visibility of the One Planet Laureates through diverse opportunities to amplify African voices, access and contribute to national, regional and global conversations, storytelling, and media engagement. Following the five-year of activity implementation in Africa and Europe, the One Planet Fellowship has mobilized over 400 scientists across Africa and Europe and facilitated their involvement in a range of interventions, including three-tiered mentoring programs, leadership, science and negotiation training series, Advanced Science Training (AST), science-policy linkages via learning visits, facilitating scientific networking and exchange between African and European scientists. [1] The Bill and Melinda Gates Foundation, the BNP Paribas Foundation, the Canada’s International Development Research Centre (IDRC) and the European Union provided financial support for the One Planet Fellowship implementation jointly coordinated by African Women in Agricultural Research and Development (AWARD) based in Nairobi, Kenya and Agropolis Fondation based in Montpellier, France.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.004 | 0.044 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".