Alternative methods of attracting investments in the gold mining industry: Russian and foreign experience
Bibliographic record
Abstract
The article reveals the possibilities of alternative ways of attracting investments in gold mining. It has been determined that at present the most popular way of attracting funds to the project is to organize and conduct an Initial Coin Offering (ICO). The paper analyzes the ICO market, with 8 gold mining companies participating in the results of 2017–Q3 2019, half of which successfully conducted ICO and implement their projects to develop the or e deposits they discovered; 3 more companies are conducting ICO now. The analysis of the completed ICO projects displays that most companies equate the placed coin (token) with the cost of 1 kg of gold. Within the study parameters, the authors also review the existing studies concerned with investment in the gold mining industry. It has been found that that at present there are no works devoted to the study of the new economic and legal phenomenon of ICO projects in the field of investing in the gold mining industry. During the research the statistics of the World Gold Council were analyzed in order to identify the countries that lead the market in gold mining. In accordance with the sample of the leading countries for gold mining, the authors review the legislation of these states, which regulates the organization and conduct of ICO by gold mining companies. As a part of this study, the legislation and economic characteristics of Australia, the Russian Federation, the United States of America and Canada are examined. The paper reveals the general trends that enable to point out the aspects that need to be taken into account when organizing ICO by such companies. For the conditions of the Russian Federation certain practical recommendations were developed aimed at establishing the regulation of the ICO as a new economic and legal phenomenon with a view to increasing the investments inflow into the gold mining industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".