IMPROVING THE EFFECTIVENESS OF MANAGEMENT IN THE FIELD OF HOUSING AND COMMUNAL SERVICES: FOREIGN EXPERIENCE
Bibliographic record
Abstract
The article discusses the experience of selected countries (Canada, USA, Sweden, Finland, France, great Britain) in the management of social indicators of quality of life in the housing utility complex (HUC); the conclusion is that in Russia it is necessary to change the control system for the FCC and the evaluation of management effectiveness. It is revealed that. the US experience is focused on effective achievements in this area; the UK experience allows us to highlight the constantly updated “ standards of the greatest value of public services”; the Swedish experience suggests using models of housing and communal services management that interact with the authorities. It was revealed that in Finland housing and communal services (housing and utilities) is a business. In the research the principles of assessing the performance of managers taking important public decisions in the housing sector, examines not only economic but also social efficiency of housing services in the office. It is proposed to apply the process approach the efficiency of the management of HMO (system, expert, calculation and estimation), the model «discounts», model «depreciation», to introduce a unified system of assessment of housing management in the whole country, taking into account international experience. Expert evaluation of management efficiency in housing should be charged to public organizations, operating without the intervention of the state, municipal authorities, for example, homeowners or people with housing in hiring, which will create a competitive environment in the market of housing and communal services will lead to greater choice for consumers and improve the efficiency of managerial labor in this field.
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 | 0.005 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".