Shaping corporate social responsibility standards in the global economy: Chinese industry guidelines for responsible mineral supply chains
Bibliographic record
Abstract
Between 2014 and 2023, the China Chamber of Commerce of Metals, Minerals & Chemicals Importers & Exporters (known by its acronym CCCMC) developed a set of corporate social responsibility guidelines which aim to steer Chinese outward mining investment toward a socially responsible model. This article presents an in-depth analysis of these voluntary industry-specific corporate social responsibility guidelines. The article also unfolds the less visible processes in which a Chinese industry association proactively engages with the dynamics of multi-level governance of corporate social responsibility in global mineral supply chains. While focusing on the rising role of the CCCMC, the article takes a ‘multi-level and actor-centred’ approach to understand the development of corporate social responsibility standards in the global economy. The article finds that CCCMC guidelines are inspired by and infused with pre-existing corporate social responsibility standards, especially international standards. Furthermore, it showcases the CCCMC’s continuing follow-up measures to implement the guidelines and increase recognition from other actors in global mineral supply chains. In particular, the article finds that the CCCMC has taken substantive measures to formulate and implement applicable labour standards in global mineral supply chains.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".