High-Level Panel on Migration convenes dialogue on African perspectives in the Global Compact
Bibliographic record
Abstract
The High-Level Panel on Migration in Africa (HLPM), the UN Economic Commission for Africa (ECA) and the African Union Commission (AUC), will jointly convene a side event under the theme: “Synergies between the High Level Panel on Migration and the Global Compact on Migration” on Saturday 8 December in Marrakech (Kingdom of Morocco), in the run up to the Intergovernmental Conference to adopt the Global Compact for Migration for Safe, Orderly and Regular Migration (GCM) in Marrakech, Morocco (10-11 December 2018). Several high-level officials will take part in this event, including HLPM Chair Ellen Johnson Sirleaf; Ahmed Husen, Minister of Immigration, Refugees and Citizenship, Canada; Cynthia SamuelOlunjowon, Regional Director for Africa, International Labour Organization (ILO); Knut Vollebaek, former Minister for Foreign Affairs, Norway and Caroline Wanjiku Kihato, Migration Expert. The discussions will be guided by findings and recommendations contained in the recent HLPM report, ‘African Migration Facing Facts, Embracing Opportunities and Mitigating Challenges.’ Once endorsed by relevant African Union decision-making bodies, the report will be submitted to the AU Heads of State and Government Summit in January 2019 for adoption.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.026 | 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".