Integration of joint consumption mechanisms as a factor in the transformation of housing policy in the reconstruction period
Bibliographic record
Abstract
The post-war reconstruction of the housing stock required the search for innovative approaches to providing the population with affordable housing through alternative consumption models. The study aimed to substantiate the possibilities of integrating global practices of collective housing consumption into post-war development strategies. The study was based on a comparative analysis of international cases, systematisation of theoretical foundations and development of conceptual models of adaptation. The main types of collaborative housing business models were classified, and their regional peculiarities of functioning in Germany, France, Denmark, Sweden (Europe), the USA and Canada (North America) and China (Asia) were identified. The theoretical analysis shown that the least regulated Asian markets shown the highest returns of up to 30%, while the tightly controlled European markets demonstrated 12-15% profitability. A review of Danish, Swedish, and Norwegian collective housing projects presented the potential to reduce household expenses by up to 45% and cut social spending by a fifth. A systematic analysis of Ukrainian market trends in 2020-2024 indicated a nearly 70% increase in housing construction, which created favourable conditions for diversifying housing supply models. Key groups of potential consumers of new housing services were identified, including a third of a million internally displaced persons in Lviv, Ivano-Frankivsk and Zakarpattia regions. Recommendations for creating regulatory sandboxes within the framework of the Diia City initiatives and launching municipal programmes to support social entrepreneurship in the housing sector were developed. The readiness of the Ukrainian digital infrastructure for the functioning of residential sharing platforms was determined, incorporating the high level of digitalisation of the population. A multi-component system for adapting foreign experience to national cultural, economic and legal conditions was developed. The practical results can be used by local authorities to develop effective housing innovation programmes and create a favourable environment for the operation of collective consumption platforms
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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 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".