Principles for responsible metals supply to electronics
Bibliographic record
Abstract
Purpose This paper seeks to critically analyse the list of principles on the extractive phase of the electronics supply chains, proposed for consumer electronic companies, by the non‐governmental campaign MakeITfair. The purpose is to understand whether conformance with these principles could positively influence the socio‐environmental conditions at the mining level. Design/methodology/approach The paper reviews the literature on incorporation of corporate social responsibility in supply chain management. It then examines how metals are mined, traded and used in electronics, as well as how the mining industry has been managing its own socio‐environmental problems. This information underpins the qualitative discussion of the principles. Findings MakeITfair's principles were found to be constructive insofar as they draw the attention of electronic companies to their shared responsibility for the problems of distant‐tier suppliers. Nevertheless, some principles may lead to potentially undesired outcomes such as biased prioritization of mining companies or regions, adoption of contentious “standards”, and conflicts concerning the sovereign rights of nations over their natural resources. Overall, the principles stress traceability mechanisms as means of influencing the mining phase of supply chains without considering the costs and benefits of overcoming the complexities involved in the metal trade and other barriers. The paper concludes by highlighting the need to consider additional ways of positively influencing metals supply. Research limitations/implications The paper points out specific research priorities in the value chains of metals. Originality/value The paper provides a critical analysis of intricate responsibility issues in the supply chain of the world's top electronic companies.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".