Prioritizing sustainability strategies for global extractive sector firms
Bibliographic record
Abstract
Purpose This paper seeks to examine the importance of corporate social and environmental initiatives to extractive sector firms and by measuring the level of social, environmental and economic sustainability in 128 countries around the world and applying these measures to a framework comprised of a sustainability indices matrix, and identifying certain strategic approaches to social and environmental practices. Design/methodology/approach The matrix contains eight categories of sustainability attainment and a k‐means cluster analysis is employed to identify what countries belong to each of these categories and to what extent these clusters identify countries with similar characteristics that may impact the focus of corporate social and environmental performance practices for extractive sector firms wishing to pursue projects in those countries. Findings The study finds that, in those jurisdictions where social and environmental sustainability is well established, extractive sector firms are required to deal with established rules and regulations that require a more reactive strategic approach. The various combinations of sustainability levels amongst the many countries around the globe require various combinations of strategies related to corporate social and environmental performance. Practical implications The realization that, today, extractive sector firms who choose to ignore the need for appropriate corporate social and environmental performance are risking increased costs arising from social and environmental damage created by their projects supports the need to create pro‐active strategies for addressing social and environmental responsibility. Originality/value This paper's contribution is the development of a framework for measuring the component levels of sustainable development and clustering a large number of countries into specific categories with recommended approaches to social and environmental sustainability strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".