Jumping the Chain: How Downstream Manufacturers Engage with Deep Suppliers of Conflict Minerals
Bibliographic record
Abstract
Global manufacturing firms are engaging distant suppliers of critical raw materials to participate in responsible sourcing. Downstream firms are concerned about risks in mineral supply chains of violent conflict, human rights violations, and poor governance, but they are limited in seeing their suppliers. Descriptive data on 323 smelters and refiners of tantalum, tin, tungsten, and gold (the “conflict minerals”) were complemented by interviews with downstream firms in the electronics industry. Results provided a narrative of supplier engagement, describing tactics used to identify “deep suppliers” at chokepoints in metals supply and to persuade producers into joining due diligence programs. Top-tier firms collaborate through a standards program to overcame barriers of geography and cultural distance in supply chain management beyond the visible horizon. Curiously, manufacturers do not need line-of-sight transparency to lower-tier suppliers. Rather, top-tier firms are “jumping the chain” to engage directly with “deep suppliers” who may—or may not—be their own actual physical suppliers. The research contributes empirical evidence to understanding multi-tier supply chains, examines how power is exercised by top-tier firms managing suppliers, and provides insights on supply chain transparency. Responsible sourcing, based on due diligence guidance and standards, is becoming expected of companies that are involved in supply chains of raw materials.
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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 | 0.004 | 0.011 |
| 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 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".