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Record W2347107049 · doi:10.1177/1065912916640900

Political Socialization and Voting

2016· article· en· W2347107049 on OpenAlexaff
Elisabeth Gidengil, Hanna Wass, Maria Valaste

Bibliographic record

VenuePolitical Research Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsMcGill University
FundersAcademy of Finland
KeywordsGeneralizability theorySocializationTurnoutVotingEducational attainmentEuropean Social SurveyPsychologyAffect (linguistics)Voting behaviorPoliticsSocial psychologyPolitical scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

Status transmission theory represents an important challenge to social learning theory, but its generalizability may be limited to countries where there is a strong intergenerational correlation in educational attainment. Based on a unique data set that matches register data from the 1999 Finnish parliamentary elections with individual-level data provided by Statistics Finland for a sample of eighteen- to thirty-year-olds and their parents, we assess these two explanations for unequal turnout. We first show that parental education does affect the turnout of young adults, as predicted by status transmission theory. However, parental voting rather than the transmission of education from parent to child appears to be the more important mediating factor. We then go on to demonstrate that there is a strong association between parental voting and the turnout of their adult children that is independent of the effects of parental education. More detailed tests of a number of implications derived from social learning theory reinforce our conclusion that the theory offers a superior explanation in countries where there is not a strong parent–child link in educational attainment.

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 score0.008
Threshold uncertainty score0.027

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.237
GPT teacher head0.521
Teacher spread0.284 · 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

Citations101
Published2016
Admission routes1
Has abstractyes

Explore more

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