Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Young scientists are a powerful resource for change and sustainable development, as they drive innovation and knowledge creation. However, comparable findings on young scientists in various countries, especially in Africa and developing regions, are generally sparse. Therefore, empirical knowledge on the state of early-career scientists is critical in order to address current challenges faced by those scientists in Africa. This book reports on the main findings of a three-and-a-half-year international project in order to assist its readers in better understanding the African research system in general, and more specifically its young scientists. The first part of the book provides background on the state of science in Africa, and bibliometric findings concerning Africa's scientific production and networks, for the period 2005 to 2015. The second part of the book combines the findings of a large-scale, quantitative survey and more than 200 qualitative interviews to provide a detailed profile of young scientists and the barriers they face in terms of five aspects of their careers: research output; funding; mobility; collaboration; and mentoring. In each case, field and gender differences are also taken into account. The last part of the book comprises conclusions and recommendations to relevant policy- and decision-makers on desirable changes to current research systems in Africa.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it