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Record W2164723744 · doi:10.25336/p6np5p

Earning and caring: demographic change and policy implications

2002· article· en· W2164723744 on OpenAlexafffundvenue
Roderic Beaujot

Bibliographic record

VenueCanadian Studies in Population · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsWestern University
FundersUniversity of Alberta
KeywordsUnpaid workPrivate sphereDivision of labourWork (physics)FertilityDemographic economicsPaid workSociologyPublic sphereInterpersonal communicationWomen's workSocial psychologyLabour economicsPsychologyEconomicsPopulationPolitical scienceDemographyWorking hours

Abstract

fetched live from OpenAlex

Seeking to define families as groups of people who share earning and caring activities, we contrast theoretical orientations that see advantages to a division of labour or complementary roles, in comparison to orientations that see less risk and greater companionship in a collaborative model based on sharing paid and unpaid work, or co-providing and co-parenting. It is important to look both inside and outside of families, or at the changing gendered links between earning and caring, to understand change both in families and in the work world. It is proposed that equal opportunity by gender has advanced further in the public sphere associated with education and work, than in the private family sphere associated with everyday life. Time-use data indicate that, on average, men carry their weight in terms of total productive time (paid plus unpaid work), but that women make much more of the accommodations between family and work. Fertility is likely to be lowest in societies that offer women equal opportunity in the public sphere but where families remain traditional in terms of the division of work. Policies are discussed that would reduce the dependency between spouses, and encourage a greater common ground between men and women in earning and caring.

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)0.000
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 score0.438
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.130
GPT teacher head0.344
Teacher spread0.214 · 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

Citations9
Published2002
Admission routes3
Has abstractyes

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