ПЕРСПЕКТИВИ РОЗВИТКУ ЕКОНОМІКИ СПІЛЬНОЇ УЧАСТІ В УКРАЇНІ
Bibliographic record
Abstract
The state of development of the sharing economy in the countries of Europe is researched and the theoretical principles of implementation and functioning of the sharing economy platforms in Ukraine is highlighted. Housing, transport, household services, as well as professional services and finance are the largest sectors of sharing economy. About half of all expenditures are expenditures on groceries and on eating outside the home as the study of the structure households’ expenditures showed. The ability of most people to spend money on holidays, purchasing cars and home appliances is limited due this condition. The programs that are offered within the framework of the sharing economy provide an opportunity for these people to meet their needs. The change of generations is an important social cause of the emergence of sharing economy. Almost a quarter of Ukrainian population are young people aged 18 to 34, who can create effective social communities and who are potential participants of sharing economy programs. Ridesharing services (services of joint trips), car sharing and household services are promising directions of the development of sharing economy in Ukraine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.079 |
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; both teacher heads agree on what is shown here.
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".