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Record W2227704132 · doi:10.3917/pope.1503.0393

The Demographic Situation in France: Recent Developments and Trends over the Last 70 Years

2015· article· en· W2227704132 on OpenAlexaboutno aff
Magali Mazuy, Magali Barbiéri, Didier Breton, Hyppolyte d'Albis

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2015
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyResidenceQuarter (Canadian coin)Life expectancyFertilityPopulationMetropolitan areaBirth rateGeographySociology

Abstract

fetched live from OpenAlex

On 1 January 2015, the population of France was 66.3 million (of which 64.2 million in metropolitan France), an increase of 0.45% with respect to the previous year. Fertility increased slightly, to 2.0 children per woman. Women had children at a mean age of 30.2 years, and men at 33.1 years. Nearly six in ten children were born outside marriage. Net migration remained quite stable. Among residence permits issued to migrants in 2013, half were granted for family reasons, slightly less than a quarter for educational purposes, 10% for humanitarian reasons, and 7% for work-related reasons. The number of marriages (among opposite-sex couples) continued to decrease slightly. Marriage was opened to same-sex couples on 17 May 2013. Between that date and the end of 2014, 17,000 same-sex marriages were registered. The seasonality of marriages remained fairly stable, while the annual peak in civil partnerships (PACS) previously observed in the second quarter shifted to the end of the year. Mean age at marriage continued to increase, reaching 34.6 years for women and 37.2 years for men in 2013. According to provisional estimates, the number of deaths in 2014 totalled 559,300. Women’s life expectancy was 84.7 years and that of men was 79.2 years, a gap of 5.5 years that has been narrowing over time.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.029
GPT teacher head0.316
Teacher spread0.287 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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