African media awards for information society reporting:
Bibliographic record
Abstract
The Economic Commission for Africa (ECA) and its partners will today present the African Information Society Initiative (AISI) Media Awards to prominent media practitioners and organizations that have made significant contributions to the development of information and communications technologies (ICTs) and the information society in Africa. The AISI Media Awards are established to encourage more informed coverage of the information society and ICT for development issues in Africa as part of the its AISI Outreach and Communication Programme. The AISI Media Awards is aimed at individual journalists and media institutions based in Africa that are "promoting journalism which contributes to a better understanding of the information society in Africa". AISI is aimed at supporting and accelerating socio-economic development across the continent, focusing on priority strategies, programmes and projects that can assist in the sustainable build up of an information society in African countries. This requires the development of information resources to reflect the needs of each and every sector and stakeholder in society.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".