Assessing the African Union's 2016–19 Human Rights Action Planning Process: Embracing, and De-Coupling from, the Conventional “Ideal”
Bibliographic record
Abstract
Abstract This article assesses the African Union's planning process regarding the development of the African Human Rights Action Plan (AHRAP) against the dominant or conventional “ideal” or model of human rights action planning. It examines the extent to which the AU's process followed or departed from the conventional model, the strengths and weaknesses of the AU human rights action planning process, and the lessons scholars and policymakers have learned about more effective and more locally responsive human rights action planning. In doing so, the article sequentially addresses the following specific themes: human rights action planning as a concept and its essential elements; the key characteristics and features of the conventional “ideal” human rights action planning process; and the extent to which the AU plan conformed to or departed from this conventional process, and its import. It also teases out some key insights and lessons learnt (in terms of strengths and weaknesses) in respect of the AHRAP planning process.
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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 | 0.103 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".