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Record W6992450651

Low fertility Lite in Canada: The Nordic Model in Quebec and the U.S. Model in Alberta

2009· article· en· W6992450651 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsFertilitySubsidyContext (archaeology)Total fertility rateSocial securitySocial policyWork (physics)Unpaid work
DOInot available

Abstract

fetched live from OpenAlex

Among the factors that are responsible for low fertility, the risks experienced by young people are particularly relevant. In that context, it is noteworthy that fertility is rising most in Alberta and Quebec, that is in provinces where young families have had the security of either good job opportunities or supportive social policy.\nThe fertility trend in Canada has seen a low point of 1.51 in 2002, rising to a total fertility rate of 1.59 in 2006. The trends and differences are placed in the context of family and work questions, including the division of paid and unpaid work by gender. Actual and intended fertility vary especially by marital status and family structure, with lower fertility in situations of less stability. Given the concurrent models of family and work, fertility varies less by women’s work status. We summarize the changing policy context, proposing that social policy has become more supportive of families with young children, especially in Quebec but also in the rest of Canada.\nThe further policy support for families needs to pay attention to the heterogeneity in the population, and thus to include subsidizing the direct costs of children, along with parental leave and child care. Family formation will also be enhanced through approaches that reduce the risks experienced by young people, and thus the importance of employment security, job satisfaction and affordable housing.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.283
Teacher spread0.230 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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