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Record W2796161422 · doi:10.1080/17457289.2018.1454452

Can you deliver a baby and vote? The effect of the first stages of parenthood on voter turnout

2018· article· en· W2796161422 on OpenAlexaff
Yosef Bhatti, Kasper Møller Hansen, Elin Naurin, Dietlind Stolle, Hanna Wass

Bibliographic record

VenueJournal of Elections Public Opinion and Parties · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsMcGill University
FundersDanmarks Frie ForskningsfondKnut och Alice Wallenbergs StiftelseVetenskapsrådetAcademy of Finland
KeywordsVoter turnoutTurnoutDemographic economicsPsychologyPolitical scienceEconomicsVotingPoliticsLaw

Abstract

fetched live from OpenAlex

Becoming a parent is a profound change in one’s life that likely has consequences for political mobilization. This paper focuses on the earliest stages of parenthood, which have rarely been theorized nor empirically investigated. Close to childbirth, there may be substantial demobilizing effects due to hospital stays, immediate childcare responsibilities, parenting distress and the physical burden of pregnancy and childbirth. It is unclear how sizeable these effects are on political demobilization as well as the extent to which they are long-lasting. Based on two individual-level register datasets from Denmark and Finland, we compare the voter turnout among parents in local elections across different dates of childbirth. We find a robust negative short-term effect. We also find that the recovery periods after childbirth are differentiated by gender, illustrating a somewhat stronger demobilizing effect of early stages of motherhood compared to the early stages of fatherhood. There are also some indications that recovery periods after childbirth are slower for women with higher socioeconomic backgrounds. Our study shows that childbearing and childbirth have strong demobilizing, although mostly temporary, implications for electoral participation, even in these strong welfare states.

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 score0.080
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.024
GPT teacher head0.287
Teacher spread0.263 · 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

Citations35
Published2018
Admission routes1
Has abstractyes

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