International Accountability in the Implementation of the Right to Development and the “Wonderful Artificiality” of Law: An African Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The landscape for the implementation of the right to development has undergone significant transformative shifts with the recent establishment of a new expert mechanism on the right to development by the UN Human Rights Council, and the finalisation of a draft treaty on the right to development. Yet, much more can clearly still be done to strengthen UN, state and non-state actors thinking on accountability in the implementation of the right to development, to add to the already considerable progress that has taken place. Our paper explores what can be done, focusing on the African and international context. We conclude that by reflecting on the benefits which a greater focus on accountability in UN development thinking post-2015 can bring to the table, the chances of success of the right to development is heightened.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it