Solving problems of garbage and waste disposal as a criterion of public administration efficiency
Bibliographic record
Abstract
The article analyzed the main problems of the world development related to the increasing pollution of oceans, land and space at the hands of human population and as a result of the activities of legal entities which are producers of the most types of products. Inter-country (by the example of the USA, Canada and some European countries) comparison of the experience in solving the waste management problem in the context of legislative, economical and organizational measures was made. The authors suggested several indicators as the criteria of assessment of efficiency of the system of the measures for prevention of waste formation, recycling, removal and reuse. The problematics in the Russian Federation was also assessed, including the progress of the “Ecology” national project implementation, and the recommendations regarding increase of efficiency of the state and municipal management in this field of social development in dual context of the commitment to the experience of the North American continent and own way of formation of a new model of waste management, with specialized hubs located near large cities and industrial centers acting as central cores, as well as creation of the incentive system for individuals and legal entities regarding the employment of separate waste collection technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".