Identifying the effects of large catastrophic shocks on the distribution of births using a combination of Benford’s law and the Vector Error Correction Model(VECM)
Bibliographic record
Abstract
This study examines the case of Romanian births, jointly distributed by age groups of mother and father, covering the period 1958-2022, under the potential influence of significant disruptors. Demographic shocks like armed conflicts, epidemics, floods, or slave trade are already present in the literature. Therefore, our study searches for the effects of World War II, the 1966 Anti-abortion Decree and COVID-19 shocks on birth distribution. Other legislative and political changes are not marginalized. Applying First Digit Law of Benford we search for anomalies in birth data. Then, following a vector-autoregressive method, we search for a long-term relation between fertility rate and anomaly in birth distribution. We also try to link disruptors and their potential effects as well. We found a statistically significant long term relation between fertility rate and birth distribution by age of parents. We confirm World War II as a major shock, and our results suggest adding the 1966 Anti-abortion Decree to the list of catastrophic events. The current work also reveals a time lag of 15 years between shock and its effects and a persistence of 15 to 20 years. COVID-19 does not impact (yet) the birth distribution by age of parents.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".