A review of methods and data to determine raw material criticality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The assessment of the criticality of raw materials allows the identification of the likelihood of a supply disruption of a material and the vulnerability of a system (e.g. a national economy, technology, or company) to this disruption. Inconclusive outcomes of various studies suggest that criticality assessments would benefit from the identification of best practices. To prepare the field for such guidance, this paper aims to clarify the mechanisms that affect methodological choices which influence the results of a study. This is achieved via literature review and round table discussions among international experts. The paper demonstrates that criticality studies are divergent in the system under study, the anticipated risk, the purpose of the study, and material selection. These differences in goal and scope naturally result in different choices regarding indicator selection, the required level of aggregation as well as the subsequent choice of aggregation method, and the need for a threshold value. However, this link is often weak, which suggests a lack of understanding of cause-and-effect mechanisms of indicators and outcomes. Data availability is a key factor that limits the evaluation of criticality. Furthermore, data quality, including both data uncertainty and data representativeness, is rarely addressed in the interpretation and communication of results. Clear guidance in the formulation of goals and scopes of criticality studies, the selection of adequate indicators and aggregation methods, and the interpretation of the outcomes, are important initial steps in improving the quality of criticality assessments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it