Applying the SCAN methodology to the semiconductor supply chain
Bibliographic record
Abstract
The SCAN ('Supply Chain Alert Notification') methodology has been developed to report signs of distress in supply chains relying on trade data. This methodology is based on a set of structural indicators to assess the ex-ante systemic risk of disruptions, and on high-frequency indicators detecting price increases and/or sizeable reductions in traded volumes. The SCAN is here applied to a basket of 74 products traded in different segments of the semiconductor supply chain, from raw materials to the final products of the chain. We find that ten products belonging to the semiconductor supply chain can be considered in medium or high risk of import disruption due to high import concentration and low substitutability in year 2021. These products belong to various segments of the value chain: raw materials, inputs for the production of wafers, equipment for the manufacture of semiconductors, and semiconductor devices. According to the SCAN methodology, EU imports of six of these products can also be considered in high distress due to observed reductions in import quantities accompanied by increases in import prices in the last available quarter (Nov. 2022 - Jan. 2023).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".