Зарубіжний досвід державного стимулювання щодо створення нових робочих місць.
Bibliographic record
Abstract
The article deals with the experience of foreign governmental incentives to create new jobs.Determined effective government incentives aimed at creating new jobs.In order to increase the probability of growth of new jobs in tight spaces identified the need for additional government incentives that focus on supporting new local innovative businesses, promote the territory and attract investment.The conclusion is that in today's labor market it is necessary to ensure continued public interest in the creation of new jobs in innovative enterprises with high technological level of development.It is now under decentralization, each community should be interested in the economic growth of its own territory, taking into account national priorities.The development priorities of the projected investment and innovation policy and employment policy is based on targeted programs and employment programs for each individual territory.These programs should not be formal and provide for the long period of time.They should have a clear plan, development strategy and be effective for a constant monitoring and adjustment depending on the social and economic situation, which consists in the local community.The aim of these programs should be to create a socio-economic, legal and organizational measures that ensure the achievement of the identity of labor demand and its supply in a particular period of time (quarter, half-year, year).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".