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Record W4205169833 · doi:10.1553/0x003d0a9e

Fertility in Austria: Past, Present and the Near Future

2021· article· en· W4205169833 on OpenAlexaboutno aff
Tomáš Frejka, Jean‐Paul Sardon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsFertilityQuarter (Canadian coin)DemographyContext (archaeology)Sub-replacement fertilityTotal fertility ratePopulationGeographyBirth ratePopulation ageingHistoryPolitical scienceDemographic economicsSociologyFamily planningEconomicsResearch methodology

Abstract

fetched live from OpenAlex

In the European context Austria’s population has a tradition of lowfertility. Between the world wars of the 20th century Austria had the lowestfertility in Europe. It recovered most notably during the 1950s and early 1960s,but has been declining ever since. Contemporary childbearing trends and patternsare characterised by a continuing delay in childbearing which started with womenborn in the late 1940s. Women born during the 1970s had lower fertility than anyprevious cohorts. Whether they were postponing their births and/or many of themdeciding not to have any children remains to be seen. In the cohorts of the mid- tolate 1960s only about three-quarters of all women had a first birth and aroundone-quarter of Austrian women remained childless. This is one of the highestknown proportions of childless women in Europe. At the turn of the century idealand actual expected family size were among the lowest in Europe, 1.7 and 1.5children per couple, respectively. Childbearing behaviour of its young inhabitantssuggests that Austria will reassume the tradition of having one of the lowestfertility levels in Europe during the initial years, possibly decades, of the 21stcentury. If these low fertility levels were to persist, a considerable decline inpopulation size as well as rapid population ageing are inevitable implying theneed for societal and policy adjustments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.384

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.284
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2021
Admission routes1
Has abstractyes

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