Shareholder engagement in the extractive sector
Bibliographic record
Abstract
The extractive industry sector has become one of the most prominent areas for shareholder engagement. Given its environmental and social impacts, and global nature, this sector's operations are particularly prone to financially material reputational risks. Large-scale investors and financial analysts concerned with reputational risk as a consequence of insufficient environmental, social and governance (ESG) standards in companies are increasingly turning to shareholder engagement as the preferred and most direct method of implementing, monitoring and advising companies. This article argues that shareholders have some impact on ESG issues with companies in the extractive sector. Their influence stems from the legitimacy they bring to the engagement process, with a high degree of knowledge in the sector and a pragmatic approach that recognizes the incremental pace of change in extractive companies. In this article we build on the work of both Mitchell and others, and Gifford on stakeholder saliency, in order to assess the results of engagement at the level of the firm, with particular reference to the extractive sector. Previous work has focused on the saliency of such engagement at the stakeholder level, with the investor as the unit of analysis. We investigate the impacts and perceptions of shareholder engagement in the extractive sector examining engagements NEI Investments with Canadian mining giant Barrick Gold from 2005 to 2009. We further quantify the results of the engagement using data from a third-party rating agency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".