Study on Net Investment in the National Economy in 2017
Bibliographic record
Abstract
As we know, economic growth can be achieved either on the account of consumption or on the account of investment, access to community funds or the middle, mixed, by establishing realistic proportions between consumption and investment so as to ensure an increase in the level of living as well as increasing job-creating investments to absorb the unoccupied population, to create a wider tax base and much more. In this article I made a study on the net investments made in the national economy that were materialized in new construction works, the purchase of machinery, including means of transport, as well as other expenses that are made in this field. Also, referring to housing construction, it was mentioned how it evolved in the fourth quarter of 2017 as compared to 2016, how it evolved throughout the year, and especially how these investments materialized in finished work on environments residence and funding resources, with the emphasis being on the fact that there is a higher share of urban investment in the construction of housing. Further analysis of the housing completed by development regions was made, emphasizing that Bucharest-Ilfov, the North West and the North-East area were the most active in developing such investments. In the study, the authors used a series of data published by the National Institute of Statistics, revealing on the basis of series of data and graphic representations the issues discussed in this article. It is revealed that in principle these investments have increased with the exception of the fourth quarter of 2017, when compared to the same quarter of 2016, both in urban and rural areas but also in the use of private and public funds – have registered some decreases. Probably this is also due to the fact that climate factors, temperatures, and so on. did not make it possible to use the possibilities of continuation of construction works. The study is balanced and reveals successive developments in this area, based on seasonally adjusted gross or series data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".