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Record W3162436946 · doi:10.1080/02601370.2021.1924302

Gender, education, and labour market participation across the life course: A Canada/Germany comparison

2021· article· en· W3162436946 on OpenAlexafffundabout
Lesley Andres, Wolfgang Lauterbach, Janine Jongbloed, Hartwig Hümme

Bibliographic record

VenueInternational Journal of Lifelong Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLife course approachNormativeEducational attainmentVocational educationGermanDemographic economicsInequalitySociologyPsychologyEconomic growthEconomicsPolitical scienceGeographyDevelopmental psychology

Abstract

fetched live from OpenAlex

In this paper, we employ a comparative life course approach for Canada and Germany to unravel the relationships among general and vocational educational attainment and different life course activities, with a focus on labour market and income inequality by gender. Life course theory and related concepts of ‘time,’ ‘normative patterns,’ ‘order and disorder,’ and ‘discontinuities’ are used to inform the analyses. Data from the Paths on Life’s Way (Paths) project in British Columbia, Canada and the German Pathways from Late Childhood to Adulthood (LifE) which span 28 and 33 years, respectively, are employed to examine life trajectories from leaving school to around age 45. Sequence analysis and cluster analyses portray both within and between country differences – and in particular gender differences – in educational attainment, employment, and other activities across the life course which has an impact on ultimate labour market participation and income levels. ‘Normative’ life courses that follow a traditional order correspond with higher levels of full-time work and higher incomes; in Germany more so than Canada, these clusters are male dominated. Clusters characterised by ‘disordered’ and ‘discontinuous’ life courses in both countries are female dominated and associated with lower income levels.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.429
Teacher spread0.393 · 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
Published2021
Admission routes3
Has abstractyes

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