Bibliographic record
Abstract
Reviewing the 360 submissions to the GenARDIS small grants fund, drawing up a shortlist of about 50 candidates and selecting the nine winners were the tasks of the six members of an international jury: ul \n\n\n\n\nli \n\n\nGesa Wesseler, planning officer, CTA, The Netherlands;\nli \n\n\nJulie Ferguson, programme officer, Knowledge Sharing, IICD, currently programme leader, Knowledge Sharing, Hivos, The Netherlands;\nli \n\n\nRamata Thioune, knowledge analyst, Acacia Initiative, IDRC, Canada/Senegal; \nli \n\n\nHelen Hambly Odame, research officer, International Service for National Agricultural Research (ISNAR), currently at the University of Guelph, Canada;\nli \n\n\nAida Opoku-Mensah, team leader, Promoting ICTs for Development, UN Economic Commission for Africa (UNECA), Ethiopia; and\nli \n\n\nFackson Banda, regional director, Panos Southern Africa, Zambia.\n/ul \n\n\n\nIn addition, Lizette Michaels at the African Training and Research Centre in Administration for Development (CAFRAD) provided invaluable administrative support, from the announcement of the GenARDIS small grants fund through to the final project reports.\nThe selection process enabled the creation of a new international support network for work on gender and agriculture in the information society. By sharing resources and exchanging views on the submissions, the agencies were able to make more cost-effective use of their funds, identify areas of mutual interest and avoid duplication in the allocation of resources.\nSmall competitive grants are increasingly recognized as a way to make better use of scarce resources. Small grants tend to encourage creativity and provide the recipients with funds that require minimal paperwork so that they can get on with their activities. Donors are also able to share the obvious risks associated with funding pilot projects. The partners involved in GenARDIS hope to prove that this process of collaboration will inspire innovation in the field of ICTs and development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".