Conventional conceptions of the African system for the promotion and protection of human and peoples' rights
Bibliographic record
Abstract
Introduction What I want to do in this chapter is to show that the African system has been imagined in very similar ways as other IHIs. First, I want to show that in their attempt to understand this system, most commentators have viewed it (or one or the other of its component entities) as (a) particularly weak and ineffectual, and (b) as dysfunctional in the sense that it has not served as a panacea to Africa's human rights problems. Secondly, I want to show also that most commentators have viewed the textual/organizational reform of the African system not merely as important, but as the key , to the success of the system. And finally, I want to show that the relevant body of scholarship has, for the most part, either been overly “enforcement-centred” or excessively focused on the “voluntary compliance” analytical framework. As has been noted already, this assessment of the conventional approaches does not imply that the state compliance optic is wrong in itself. Rather, it is intended to underscore its conceptual incompleteness and the necessity for its enlargement and expansion. What will be suggested is that there is a need to reach beyond – while retaining – the state compliance-focused optic. Having already considered in chapter 2, the various approaches to the study of IHIs more generally, and having concluded that the constructivist approach best serves our purposes in this book, that whole discussion will not be repeated here.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".