Digital networks to aid research and education in Africa
Bibliographic record
Abstract
Around 120 participants were assembled over two days at CERN to discuss ways to bridge the digital divide with Africa. As part of efforts to implement the outcome of the first World Summit on the Information Society (WSIS), held in Geneva in 2003, CERN held the international workshop on Research and Education Networks in Africa, from 25 to 27 September. Organized by the United Nations University (UNU) in collaboration with the International Telecommunications Union (ITU) and CERN, this meeting was designed to promote scientific cooperation with and within Africa, through the development of networking infrastructure. Faster, reliable and more affordable Internet access is widely recognized as one of the key factors for enhancing research and education efforts in African academic and research institutions. For the first time, this workshop brought together representatives of all the key stakeholders: African academic and research institutions, international coordinators, funding agencies, grass-roots implementers and industry. The objective was to provide tangible results, in the form of concrete recommendations for action, to the Preparatory Committee for the WSIS, which is meeting in Geneva on 12-30 September in advance of the Summit taking place in Tunis in November. The 120 or so participants reviewed major networking initiatives and achievements in Africa and shared experiences on successes and failures. The workshop reviewed results of case studies from participating countries as part of a major feasibility study on a pan-African University Network (AFUNET), funded by the Norwegian Agency for Development Cooperation (NORAD). The workshop also highlighted results of recent meetings on extending African university connectivity, organized by Internet2, the US university-led research network developer, and the Canadian IDRC (International Development Research Centre). The meeting also had the aim of consulting with African academic and research institutions on their needs, strengthening collaboration between them and other stakeholders, compiling a report on case studies and agreeing on business principles for a future AFUNET. The workshop will produce a strategic plan of implementation that will be presented to the WSIS Preparatory Committee, and which will form a blue print for the way forward. CERN, where the Web was born, has traditionally supported efforts to provide better network access for scientists in developing countries and particularly supports actions to make the contents of publicly funded education and research freely accessible on the web, and to provide all universities with sufficient bandwidth so that they can exploit this information. For more information and to consult the programme, visit the workshop's website: http://cern.ch/event-africa-networking/
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 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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".