Entrepreneurial structures of the extractive industry: foreign experience in environmental protection
Bibliographic record
Abstract
Purpose. To propose measures on assessing the initial state of the environment based on the foreign experience of natural resources protection by business structures in the extractive industry (using the example of some countries that are members of the Organization for Economic Co-operation and Development (OECD). Methodology. In the course of the scientific research, the authors used a number of general scientific and special methods of cognition, such as analysis for critical assessment of approaches to the interpretation of the essence and necessity of nature protection; quantitative and qualitative comparisons to highlight the mutual impact of environmental protection measures used by companies of OECD member countries; scientific abstraction and systematization for setting out proposals regarding the application of the most successful measures for Ukraine, applied by OECD member countries. Findings. The authors considered the experience of foreign member countries of the OECD such as Kazakhstan, Australia, Canada, the USA in terms of the implementation of some international regulations on labor protection, local maintenance standards, the governments focus on cooperation with license holders for subsoil use operations. Originality. The authors suggested that the government of Ukraine pay attention to the measures introduced by Australia in terms of assessment of the initial state of the environment. Subsoil user companies should collect environmental information at the project planning stage in order to determine the factors that are subject to monitoring, further study, and control at the stage of liquidation of consequences after the termination of operations. Environmental information should include information on climatic conditions, geological data, soil data, hydrological data, data on vegetation, terrestrial and subterranean fauna, as well as information on socioeconomic conditions and cultural heritage sites. Practical value. The considered experience is also useful for Ukraine, since we have a significant part of enterprises in the extractive industry and the issues of environmental protection and nature management should occupy one of the first places in companies. The results of the research can be used by practitioners, scientists, and civil servants for further perspectives of the development.
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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 | 0.001 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".