Extractive Industries, Human Rights, and the Home State Advantage: AGovernance Framework
Bibliographic record
Abstract
This chapter by Prof. Macklin is the culminating chapter of a co-authored book, The Governance Gap: Human Rights, Extractive Industries, and the Home State Advantage (Routledge, forthcoming 2014, co-authored with Penelope Simons). John Ruggie’s Guiding Principles on Business and Human Rights is constructed on three pillars: the state duty to protect human rights within its jurisdiction, the corporate responsibility to respect human rights, and the need for effective judicial and non-judicial remedies.The author's intervention focuses on the first pillar, and looks specifically at the duty and authority of home states to regulate their transnational corporate 'citizens.' Bracketing off the important question of political will, the author asks 'what might home state regulation look like if states took seriously their duty to protect human rights?'The author answers this question with a governance template that comprises norms, monitoring, and consequences. The goal is to leverage the specific governance capacity of 'governance rich' states to support (rather than supplant) human rights protection by host states and to complement, (rather than displace) existing multi-lateral and international initiatives. The core of the model is an independent, arms-length Corporate Social Responsibility (CSR) Agency. The CSR Agency would engage in pre-investment human rights impact assessments, as well as ongoing monitoring of corporate citizens operating in weak governance zones. The incentive for corporate participation would be access to a range of public benefits that states routinely extend to corporate citizens, including export tax credit, risk insurance, consular and trade support, protection under investment agreements, etc. Compliance with the norms, as evidenced by independent evaluation and monitoring, is the prerequisite to obtaining and retaining public support. Only companies that wished to secure public support would be subject to the Agency's purview.Separate features of the model (apart from the CSR Agency) include disclosure requirements similar to those currently emerging from the US (Dodd-Frank) and the EU (Accounting Directive) and conventional public sanctions, such as criminal liability, capital market discipline and trade/investment sanctions. These mechanisms, while important, are intended to play a less prominent role. With respect to civil liability, the author proposes a statutory model that adapts principles of corporate citizenship and parent-based liability from existing tax and anti-corruption regimes, and which remains appropriately attentive to objections of extraterritoriality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".