National information and communication infrastriucture (NICI): best practices and lesson learnt
Bibliographic record
Abstract
Africa’s economic performance since the mid-1990s has raised hopes of a possible turnaround, compared to the stagnation of the previous two decades. The impact of new ICTs has permeated virtually all sectors of society. This publication analyses the work undertaken by the United Nations Economic Commission for Africa (UNECA) around national ICT strategies. It also highlights the challenges and best practices and proposes recommendations for future activities given the growing scope, scale and importance of knowledge in the global economy. ECA’s early efforts to promote ICT for Development (ICT4D) culminated in the launch and adoption of the African Information Society Initiative (AISI) at the Conference of African Ministers in charge of planning and social and economic development in 1996. In the new repositioned ECA, ICT activities have been scaled-up in member States. There is a critical mass of countries with national policies in place and ECA is assisting countries with implementation.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".