14th Session of the Regional Coordination Mechanism for Africa (RCM-Africa) of United Nations agencies and organizations working in Africa in support of the African Union and the NEPAD programme
Bibliographic record
Abstract
Welcome Remarks by Carlos Lopes UN Under-Secretary-General and Executive Secretary of ECA at the 14th Session of the Regional Coordination Mechanism for Africa (RCM-Africa) of United Nations agencies and organizations working in Africa in support of the African Union and the NEPAD programme. Mr. Lopes, on his remarks highlighted that, this year’s theme “United Nations Support to the African Union Agenda 2063” reflects the same spirit. This is in the sense that it underscores the important role that the RCM has to play in responding to emerging priorities in the continent. It moreover shows that ownership resides in the continent and the UN can only support and not drive the process. ECA has a new Capacity Development Division, which by its establishment will help to boost the resources available to the Joint Secretariat of the RCM. We envisage that it will contribute to enhancing the work of RCM clusters. The outcome of this RCM meeting would also serve to guide the preparations for that forthcoming 15th session which will be convened in Abuja in the first quarter of next year.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.201 | 0.090 |
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".