Seasonal profile of nuptiality, natality and mortality in the Czech republic and international comparison
Bibliographic record
Abstract
Seasonal profile of nuptiality, natality and mortality in the Czech republic and international comparison Abstract The aim of this thesis is to capture changes in seasonality of nuptiality, natality and mortality in the Czech Republic and the factors that influence the seasonality. Despite the fact that these changes have been monitored since the 16th (nuptiality) and the 17th century (natality and mortality), the thesis focuses on the period from 1950s to 2012. Czech Republic is then compared with six other countries, with shorter reference period (2005-2012). Seasonal profile is represented by seasonal indexes and their variability is analyzed using the coefficient of variation. Seasonality of marriages was, especially in the 1950s, characterized by the accumulation of marriages in the last months of the year and in April, with later shift to the summer. The minimum was at the beginning of the period in the first quarter of the year and in May and later joined them low values in November and December. Due to superstitions about unhappy marriages, May was a month with a very low seasonal index throughout the period. The highest values of seasonal indexes of live births used to occur in the first half of the year and then shifted, as in the case of weddings, to the summer months. The minimum values did not...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".