Presence through absence? Understanding the role of capital in the African Human Rights Action Plan
Bibliographic record
Abstract
Abstract This study examines the African Human Rights Action Plan (AHRAP) through the lens of Upendra Baxi's germinal theory on the emergence in our time of a ‘trade-related, market-friendly human rights’ (TREMF) thesis that is challenging the specific understandings of ‘people-centric’ human rights that are predicated in the letter and spirit of the Universal Declaration of Human Rights (UDH). Baxi contends, instead, that the dominant strands of the contemporary understandings of human rights are – for the most part – designed to protect the interests of global capital. That said, human rights frameworks in low-income countries need to be studied with a view to what they say and don't say about global capital. Despite its attempt to facilitate a progressive realisation of human rights in Africa, the AHRAP does not rise far enough above the TREMF paradigm to re-locate itself within the UDH one. This is due to the AHRAP not adequately theorising and analysing the role of capital in the (non)realisation of human rights in Africa. By allowing trade and market practices to slip to a significant extent beyond its purview, the AHRAP privileges – to a significant degree – the needs/interests of capital over the human rights of ordinary Africans. That is, the victims of the excesses of capital in Africa are reincarnated in the AHRAP document by the fact of their exclusion from it.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.071 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".