CONFCAYS 2019 & Sub Regional Training Workshop on Science Advice Science Advice in Africa: Opportunity or illusion
Bibliographic record
Abstract
The Cameroon Academy of Young Scientists (CAYS) is a branch of the Cameroon Academy of Science (CAS) aimed at promoting research, paving the way for young scientists, encouraging the development of innovative approaches to national and international challenges. CAYS is a forum for building scientific capacities and the applicability of science to solve problems and provide decision makers and the public with advice based on the most up-to-date scientific knowledge. With support from MINRESI, MINTOUR, MINESUP, CAS, AUF and other partners, CAYS is organizing it first biennial international conference under the theme «Young Scientists: Mainspring of innovation and development in Africa » where scientists have opportunities to present their research results on diverse topics. More and more, scientific advice is spreading the world over to assist policy makers and politicians to make decisions that are informed by evidence-based data and scientific knowledge. It is an opportunity for dialogue that breaks or limits the gaps between researchers in different fields. In Africa and particularly in the Central African Sub-region, the progress of this concept is still lagging behind. Taking cognizance of this, CAYS offers an opportunity, during this conference, of a training workshop on the science advice in collaboration with The Quebec Research Funds (FRQ) and the International Network for Government Science Advice (INGSA). For the workshop, 65 participants were selected from government institutions and civil society organizations in the Central African Region. A total of 153 abstracts (55 oral and 98 poster presentations) were selected from national and international participants. The said abstracts will be presented in 4 sessions under 3 major themes including 1) Nutrition, Health and Environment; 2) Material Science, ITC and Renewable Energy; and 3) Digital Economy, Peace and Development. These abstracts have been compiled in this abstracts volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".