Unfinished business: legalisation and implementation in business and human rights
Bibliographic record
Abstract
The thesis explores the nature of transnational legalisation by identifying one emerging norm – corporate accountability for human rights violations – and tracing its promotion through three separate pathways of legalisation. At the domestic level, the thesis discusses the jurisprudence of domestic courts that have contemplated assuming extraterritorial jurisdiction over alleged human rights violations of transnational corporations (TNCs) in other states. At the international level, the thesis considers developments in the United Nations (UN), which in 2011 launched a new normative framework to bolster the accountability of TNCs in respect of human rights. At the transnational level, the thesis discusses the Kimberley Process Certification Scheme (KPCS), the Extractive Industries Transparency Initiative (EITI), and the Voluntary Principles on Security and Human Rights (VPs), which have been selected as representative of the range of hybrid schemes increasingly developed by government and industry representatives to ameliorate the impact of TNCs on human rights. The thesis also develops a framework with which to analyse these trends by adopting (and further developing) the liberal institutionalist tool of legalisation, which is described in Kenneth Abbott et al’s ‘The Concept of Legalisation’. This thesis argues that this classic framework can be adapted and reimagined in the context of the transnational legal system, which is characterised by thick configurations of agents working across a multiplicity of issue areas. I suggest that in applying the classic framework in the transnational context, there appears to be an omitted variable – that of implementation, which exists alongside obligation, precision, and delegation. Implementation refers to the specific actions taken by agents to translate legal or law-like principles into practical, workable instructions for courts, governments, companies and other non-state actors to follow. The thesis argues that an increased focus on implementation generally leads to more effective or greater legalisation. The empirical chapters demonstrate that efforts in implementation are often undertaken for the purpose of strengthening one or more other legalisation characteristics in the long run. This suggests that agents will be willing to accept lower levels of obligation, precision and/or delegation if they believe a focus on implementation will help strengthen these characteristics over time.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".