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Record W2887626293 · doi:10.1080/00324728.2018.1490450

The mechanics of the baby boom: Unveiling the role of the epidemiologic transition

2018· article· en· W2887626293 on OpenAlexafffundabout
Danielle Gauvreau, Patrick Sabourin, Samuel Vézina, Benoı̂t Laplante

Bibliographic record

VenuePopulation Studies · 2018
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsConcordia University
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureCanada Foundation for Innovation
KeywordsBaby boomBoomFertilityImmigrationPhenomenonDepression (economics)DemographyGreat DepressionEpidemiological transitionSurvivorship curveDemographic transitionPopulationDemographic economicsHistoryEconomicsSociologyKeynesian economicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Recent research on the baby boom and its causes has shown that common explanations, such as the recuperation of births following the Great Depression or Second World War, are not sufficient to account for the phenomenon. However, that research has stressed the role of increasing nuptiality. In this paper, we argue that the increase in survivorship of children and young people that resulted from the epidemiologic transition accounted for a large portion of the increased number of births during the baby boom. We use a microsimulation model to assess the respective roles of mortality, nuptiality, fertility, and immigration on the size and dynamics of the boom in Quebec, Canada. Results show that decreasing mortality contributed significantly to the baby boom, along with immigration and nuptiality changes, while fertility rates attenuated the phenomenon. These results substantiate the hypothesis that the epidemiologic transition was an important cause of the baby boom.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.192
Threshold uncertainty score0.382

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
GPT teacher head0.414
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2018
Admission routes3
Has abstractyes

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