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Record W6943729842 · doi:10.17045/sthlmuni.5803257

Changing Seasonal Variation in Births by Sociodemographic Factors: A Population-Based Register Study

2018· preprint· en· W6943729842 on OpenAlexaboutno aff

Bibliographic record

VenueStockholm University Figshare Repository · 2018
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSeasonalityFertilityBirth rateQuarter (Canadian coin)Variation (astronomy)Total fertility rate

Abstract

fetched live from OpenAlex

<b>Abstract:</b> Seasonality of births is in some populations strongly influenced by sociodemographic factors. In this study, we analyse the impact of mothers’ sociodemographic characteristics for the seasonal variation in 7,710,955 live births in Sweden between 1940 and 2012. During 1940-1999, Swedish birth rates showed the typical seasonal variation with high numbers of births during the spring, and low numbers of births during the last quarter of the year. However, during the twenty-first century, the seasonal variation in fertility declined so that only minor variation in birth rates between February and September remains. Still, the pattern of low birth rates at the end of the year remains and has even become more pronounced in recent decades. The roles of maternal education, mother’s birth country, parity, and instances where the mother has re-partnered between subsequent births changed during the second half of the twentieth century. The study underlines that in a society with low fertility and efficient birth control, active choices and behaviours associated with an individual’s sociodemographic characteristics tend to matter more for the seasonal timing of childbearing than environmental factors related to the physiological ability to reproduce and cultural-behavioural factors related to the frequency of intercourse.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.017
GPT teacher head0.204
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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