Bibliographic record
Abstract
Introduction In line with the conceptual and methodological approach that was adopted in the last chapter, the same kinds of issues that were raised in that context with regard to the impact of the African system within Nigeria also animate the discussion here. As the nature of those approaches and related issues have been discussed extensively in chapter 4 it will not be repeated here in any detail. What this chapter focuses on is the systematic examination and discussion of the available evidence relating to the impact of the African system within South Africa. Has this system impacted judicial reasoning and action, executive deliberations and action, legislative debate and action, and the work of civil society actors (CSAs) in South Africa? If so, to what extent has its impact been felt within these institutions and groups? What factors have facilitated or impeded this impact? Do the various processes via which this impact has been produced (what I have referred to elsewhere in this book as “correspondence”) differ from the “state compliance” which is traditionally focused upon in most of the literature? If so, how do they differ? What was the role of activist forces (such as some CSAs, judges, and MPs) in these processes in South Africa? What does the character of these other processes tell us about the adequacy or otherwise of the dominant state compliance measure and the way in which we evaluate and imagine international human rights institutions (IHIs)?
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".