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Record W4403623265 · doi:10.1093/jhuman/huae033

Can Voluntary Business and Human Rights Norms be Effective? Exploring a Multidimensional Perspective of Norm Effectiveness in Africa

2024· article· en· W4403623265 on OpenAlex
Nathan Andrews, Raynold Wonder Alorse

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Human Rights Practice · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsQueen's UniversityMcMaster University
Fundersnot available
KeywordsPerspective (graphical)Norm (philosophy)Human rightsPolitical scienceSocial psychologyPsychologyLawComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.005
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.273
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it