Bibliographic record
Abstract
Purpose The aims of this paper are: to explore the nature of political risk insurance (PRI) contracts as a form of regulation in the context of mining projects in developing countries; to examine how PRI providers factor corporate social responsibility (CSR) policies and practices of applicants in their initial decisions to provide PRI; to examine how CSR criteria are reflected in the terms of PRI contracts; to understand how failure to exercise good CSR practices by recipients of PRI affects insurance coverage; to shed light on how good CSR practices which minimize risk to companies and communities can be or are rewarded through PRI contracts; to identify opportunities for reform. Design/methodology/approach This article adopts a conceptual approach through analysis of the practical effects and public policy implications associated with use of PRI contracts as a regulatory mechanism to promote good CSR practices. Findings PRI contracts represent a form of proactive risk management used by investors. Because of the significant regulatory effect of the CSR provisions of PRI contracts provided by state‐based agencies, there is considerable potential for and value associated with greater transparency in the implementation of such contracts. Originality/value This article sheds light on the regulatory dimensions associated with the CSR provisions of PRI contracts. This represents a new contribution to the literature on CSR contracts, which until this point has focused largely on the CSR aspects of supply chain contracts.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".