MétaCan
Menu
Back to cohort
Record W1574459447

Models of Earning and Caring: Evidence from Canadian Time-Use Data

2001· article· en· W1574459447 on OpenAlexaboutno aff
Roderic Beaujot, Jianye Liu

Bibliographic record

VenueScholarship@Western (Western University) · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSpouseWifeUnpaid workGeneral partnershipDemographic economicsWork (physics)PsychologyEconomicsSociologyPolitical scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Family models can usefully consider the production and reproduction roles of women and men. For husband-wife families, the breadwinner, one-earner, or complementary-roles model has advantages in terms of efficiency/specialization and stability, but it is a high risk model for women and children in the face of the inability or unwillingness of the breadwinner to provide for (especially former) spouse and children. The alternate model has been called two-earner, companionship, “new families” or collaborative in the sense of spouses collaborating in both the paid and unpaid work needed to provide for and care for the family. When there are children, this can be called the co-provider and co-parenting model.\nAdopting the common metric of time-use to study both paid and unpaid work, the Canadian national surveys of 1986, 1992 and 1998 show that the traditional or neo-traditional models remain the most common, and the “double burden” is the second most frequent, but there is some evidence of change in the direction of more symmetric arrangements, especially for younger couples with children, when both are employed full-time. Patterns over the life course clearly indicate that women carry much more of the burden in terms of accommodating the meshing that needs to occur between productive and reproductive activities. Policies that would modernize families are discussed, including those that would reduce dependency in relationships.

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 score0.695
Threshold uncertainty score0.976

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 studies0.0010.000
Scholarly communication0.0000.005
Open science0.0010.000
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 head0.205
GPT teacher head0.332
Teacher spread0.127 · 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

Citations5
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueScholarship@Western (Western University)Same topicGender, Labor, and Family DynamicsFrench-language works237,207