"STRATEGIC MINERAL RESOURCES" - THE LEADING FACTOR OF MINERAL RESOURCES POLICY
Bibliographic record
Abstract
Against the background of the conceptual approach to the mineral resource problem, the concept of “strategic mineral resources” is generally characterized in terms of the leading factor in the mineral resource policy at the State level. The essence of this category of mineral resources and its pivotal position in the general scheme for solving the problem are revealed. It is emphasized that the problem of mineral resources is predetermined by a constantly existing contradiction between the vital necessity of the systematic consumption of mineral resources by a Human (community, state) and the restrictive access to them. The object of study is formulated by the authors of the work as the "Mineral Resource Poli-cy of the State", and the subject of the study is “Strategic Mineral Resources as the most significant part of the consumed mineral resources of the nation, considered as the leading factor in the mineral resource policy”. Consideration of the mineral resource policy on the example of a number of countries (USA, China, Russia, EU countries, Canada, Japan, etc.) shows that, although each of them is unique in this sense and has its own priorities, there are, at the same time, some similarities in understanding this problem and the ways to solve it. Herein lies a number of provisions, the analysis of which allows us to state that they represent the largest elements of efficiency ("tools") of the mineral resource policy of the "advanced" States which were developed in the process of practical activity. The studies show that the "decisive link" here is formed by the provisions related to the notion "strategic" in respect of certain types of mineral raw materials, as well as their "criticality" in terms of "supply risk" and "vulnerability from limited supply" from foreign sources. Thus, based on these empirical conclusions, it is possible to designate a “strategic line” and the main steps in solving the mineral resource problem. The decisive factor in this case, as the authors believe, is the correct allocation of the “strategic” status to the most important part of the mineral resources consumed by the nation, which allows us to create on this basis a high-ranking instrument of mineral resources policy.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".