TWINNING between Institutions in developed and less developed countries: an ideal way to set-up an astrophysics program
Bibliographic record
Abstract
Abstract It is very difficult to start from scratch a new Astrophysics program in a country with very little or no researchers in the field. In 2007, we began to set-up an Astrophysics program by TWINNING the Université de Ouagadougou with the Université de Montréal in Canada, the Université de Provence in France and the University of Cape Town in South Africa. Already, courses are given at the undergraduate and Master levels and a teaching Observatory has been built. A 1m research telescope was also moved from the La Silla Observatory in Chile to Burkina Faso and the infrastructure is being built at the moment on mount Djaogari in the north-eastern part of the country. In the meantime, 6 students are doing their PhD in Astrophysics overseas (Canada, France and South Africa) and will become the core of the research group at the Université de Ouagadougou. An engineer is also doing his PhD in Astronomical Instrumentation to help with the maintenance of the equipment on the Research Telescope.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 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".