Bibliographic record
Abstract
In this paper I aimed to outline an analysis of the evolution of the Romanian labor market. I performed a comparative analysis on labor resources and how they are used, employment, working conditions, in Romania, referring to data recorded on Eurostat. The research will be conducted at national level between 2016-2019. In the research I used the observation method based on the description of the indicators that characterize the labor market in Romania and in the EU member countries. Second, we analyzed the statistics provided by the NIS and identified measures that stimulate the growth of employment in order to achieve a sustainable development. The results show that employment in the EU continued to grow unexpectedly during the third quarter of 2017, while the unemployment rate continued to decline. In 2018, Romania registered 237.7 thousand inactive persons, the employment rate of the population aged 20-64 years was 69.9%. In the first quarter of 2019, the employment rate of the population aged 20-64 was 69.2%, down from the previous year and at a distance of 0.8 percentage points compared to the national target of 70% established in Europe 2020 strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".