Sustainability management and social license to operate in the extractive industry: The cross‐cultural gap with Indigenous communities
Bibliographic record
Abstract
Abstract This study aims to analyze the role of cross‐cultural differences between Indigenous communities and extractive organizations with regard to the sustainability measures these organizations employ. Although Indigenous communities are important stakeholders, especially in remote areas where extractive organizations are mainly located, these organizations' relationships with Indigenous communities have been overlooked in the literature on sustainability management. Drawing on a qualitative study based on 25 semi‐structured interviews with Canadian respondents, the findings show how cross‐cultural issues tend to create misunderstandings that can seriously undermine organizations' social license to operate and their initiatives for sustainability. Those issues—which include cultural differences in terms of connectedness with nature, spiritual and historical attachment to a specific territory, reliance on written documents, and conception of time—are analyzed through the lenses of practitioners with significant experience in the relationships between extractive organizations and Indigenous communities. This paper proposes measures to better manage this cross‐cultural gap. The study contributes to the literature on corporate sustainability by going back to the roots of this concept and by shedding more light on the importance of a specific category of stakeholders that tends to be overlooked in the managerial 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".