African Lessons for Post-2015 Global Right to Development Conceptualization and Practice
Bibliographic record
Abstract
MY CHAPTER in the book that we are celebrating today, the specific version of the right to development (RTD) that has become ascendant globally is deeply rooted in the academic, socio-economic and political struggles of many African individuals, peoples, and states. 1 Africa's contribution to what Upendra Baxi has strikingly described as the "development of the right to development," 2 has therefore been immense, non-the-least because Article 22 of the African Charter on Human and Peoples' Rights is one of the precious few hard law guarantees of the RTD in international law in the whole world.Thus, the "Africa-toward-the-Globe" gaze of this presentation is only fitting.What I will do in the short time that I have is to flag four important lessons that I think that those engaged in RTD conceptualization and practice ought to learn both from the pathbreaking treatment of the RTD as a binding and justiciable legal obligation within the African human rights system, and the actual real life "adjudication" of that right by the African Commission on Human and Peoples Rights.What then are these lessons?+ This is an edited version of a presentation made at the launch of the United Nation's book published in celebration of the silver jubilee of the UN Declaration on the Right to Development, entitled: Realizing the Right to Development (New York and Geneva: United Nations, 2013).This presentation was based on the chapter I contributed to this book.
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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 | 0.046 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".