ANALYSIS OF THE MARRIAGES AND DIVORCES SEASONALITY IN ROMANIA COMPARED TO BACAU COUNTY DURING 2010-2013
Bibliographic record
Abstract
It is known that the number of marriages concluded in general in a geographic area follows a cyclical trend due primarily to religious factors. In this study we wanted to test whether this assumption is maintained in the past four years. On the other hand, we wanted to look at the indices of seasonality in Romania compared to those calculated for Bacau County to see if there are significant differences. The same aspects were analyzed for the divorce evolutions, to identify whether there is a seasonal trend in this case as well. After applying the methods of arithmetic mean and the mobile averages, there were calculated the seasonality indices and concluded that marriages are clearly cyclical developments every 12 months, with a peak in the third quarter, respectively in August, when the number is 3,5 times higher than the monthly average yearly in Bacau County and 2,3 times for Romania. In the case of divorces, the evolution is oscillatory, but without identifying a seasonal component. The average monthly number of divorces per 100 marriages is 29 in Romania and 36 in Bacau, with a maximum of 202 in the county of Bacau, observed in March 2010.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".