Bibliographic record
Abstract
ENGLISH ABSTRACT: The main goal of the study was to conduct an assessment of the state of science in Tanzania. More specific objectives focused on the levels of research investment, human resources for S&T, and the research performance of the system. In addition we also investigated the challenges that young scientists in the country face. Our study shows that Tanzanian expenditure in R&D remains still below 1% of GDP and lags behind several African countries including Kenya the sister EAC country. In spite of the slight increase in spending in R&D from 0.38% in 2010 to 0.53% of the GDP in 2013, there is still overdependence on international funding sources. It was also found that the lack of research funding and funding for research equipment are the biggest challenges in the performance of research for young scientists. The study also found that Tanzania's human resources for S&T remains unacceptably small compared to several SADC countries, which results in relative low output per million of the population. However, it was revealed that there was a gradual increase in Tanzania scientific outputs from 339 publications in the year 2005 to 1389 publications in 2018 which is more than four times the growth of literature. In spite of the increase in the publications across all research fields,Tanzania dropped its position in world rank from position 74 in 2005 to position 80 in 2018. Tanzanian science remains strong in its traditional fields: the relative strength analysis revealed that the agricultural and health sciences, and to a lesser extent, the social sciences, are the most active fields compared to the world output across these fields. The overall top five prolific R&D institutions in the production of scientific papers are the MUHAS, UDSM, SUA, NIMR, and IHI. International co-authorship is on the increase in most fields, but these trends probably reflect the growing participation of Tanzanian scientists in global health and agricultural projects rather than any substantive growth in research collaboration. Our main recommendation is that the Tanzanian government commits to increasing its investment in R&D as aspired to by the R&D policy. In addition, the number of R&D personnel has to be increased to ensure that knowledge production continues to grow and the application of science, technology, and innovation for inclusive development is achieved.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".