FROM SHADOWS TO SPOTLIGHT: THE RISE OF ENVIRONMENTAL ACCOUNTABILITY IN EXTRACTIVE INDUSTRIES
Bibliographic record
Abstract
This study provides a bibliometric analysis of sustainability disclosure within the extractive industries, with a specific focus on the mining sector. Given the increasing global emphasis on environmental accountability, this research identifies key themes, influential publications, and evolving trends in sustainability reporting. Using bibliometric data from 2006 to 2021, the study analyzes patterns across several major journals, countries, and authors to highlight the development of sustainability disclosure practices. A co-occurrence network of keywords is constructed, revealing four main research clusters: environmental disclosure in China’s mining sector, the economic impacts of mining, content analysis linked to legitimacy theory in Canada, and corporate social responsibility (CSR). The thematic map further categorizes these themes by centrality and density, identifying motor themes with high centrality, emerging themes, and isolated but well-developed themes. Additionally, a thematic evolution analysis outlines the progression of environmental disclosure research in four phases, showing how focus areas have shifted from fundamental environmental concerns to more integrated sustainability reporting. The findings underscore the need for more comprehensive, cross-country studies and refined frameworks to better capture the nuances of sustainability practices in extractive industries. This research provides a valuable foundation for future studies, enabling a deeper understanding of sustainability disclosure and its role in promoting environmental responsibility in high-impact sectors.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".