Can Voluntary Business and Human Rights Norms be Effective? Exploring a Multidimensional Perspective of Norm Effectiveness in Africa
Bibliographic record
Abstract
Abstract Although the concept of human rights was rarely visible in corporate documents prior to the 2000s, many corporations today publicly espouse strong commitments to respect human rights due to normative mechanisms such as the UN Guiding Principles on Business and Human Rights (UNGPs) introduced in 2008. Contributing to ongoing scholarly discussions around the known gap between human rights rhetoric and performance, this article draws upon the global norm diffusion literature to conceptualize the effectiveness of business and human rights (BHR) norms as output, outcome, and impact. This multi-dimensional understanding of effectiveness reveals why a norm—embraced by a variety of stakeholders such as corporations, governments, and civil society groups—could still face contestation and implementation challenges at the grassroots, implying a lack of impact effectiveness. The article contextualizes this discussion within specific cases in Africa, using primary fieldwork data collected in Ghana and South Africa alongside other secondary data. Our overall objective is to contribute to both theoretical and practical discussions of how BHR norms spread and become useful to purported beneficiaries or ‘end-users’ of such norms. In doing so, the article showcases a deeper understanding and contextualization of human rights in the ‘real world’ of places where extractive corporations operate.
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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 | 0.036 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".