Bibliographic record
Abstract
This paper presents the main findings resulted from indicators analysis (gross value added, gross operating surplus, gross national income, etc.) which characterize the institutional sectors – the ones emphasizing the different behaviors and results between the competition and non-competition sectors (households and general government respectively). This analysis is very necessary, because the European Commission, through the specialized directorate - DGECFIN, has included in the forecast framework the indicators regarding incomes and expenditures of institutional sectors (compensation of employees, gross disposable income, gross saving) for member states. The macroeconomic forecast has not yet used this economic approach. The main inconvenience in estimating institutional sectors accounts forecast refers on one hand to the gap between the statistical and forecasting horizons, the statistical data regarding the institutional sectors are available only after a period of two years since the event has occurred (the data series for Romania end in 2004) and, on the other hand, the aggregates evaluation is only carried out in current prices, increasing thus the relativity of data series by using conventional deflators. Until now they are the first estimates referring to the compensation of employees and the gross disposable income. *This paper is partially based on the study “Overview of the economic results by institutional sectors”, NCP, 2007
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".