Making and Breaking the Rules in Business and Human Rights
Bibliographic record
Abstract
How do we make corporations accountable for human rights violations? This book illuminates how governments, international organisations, NGOs and individuals make (and break) the rules in business and human rights. It covers a rich array of examples of rule-making in business and human rights, including: (i) legal developments in domestic courts in the US, Canada, the UK, and Europe; (ii) initiatives endorsed by the United Nations, including the 2011 UN Guiding Principles; and (iii) multistakeholder initiatives such as the Kimberley Process Certification Scheme (KPCS), the Extractive Industries Transparency Initiative (EITI), and the Voluntary Principles on Security and Human Rights (VPs). It also introduces a new theoretical framework to assist scholars in understanding trends in the area of business and human rights. By emphasising implementation, the framework brings much-needed conceptual clarity to the processes of rule-making and legalization and constitutes an important contribution to the business and human rights literature.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".