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FAMILIES, TIME AND MONEY IN CANADA, GERMANY, SWEDEN, THE UNITED KINGDOM AND THE UNITED STATES

2007· article· en· W2091056955 on OpenAlexaffabout
Peter Burton, Shelley Phipps

Bibliographic record

VenueReview of Income and Wealth · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMicrodata (statistics)CrunchEconomicsDemographic economicsEconomic shortageWork (physics)Labour economicsWork hoursDemographyWorking hoursPopulationSociologyGovernment (linguistics)

Abstract

fetched live from OpenAlex

Using microdata from the Luxembourg Income Study, we assess “time crunch” for families with children in Canada, Germany, Sweden, the U.K. and the U.S. Both theory and empirical evidence suggest that both time and money are important inputs to the well‐being of parents and children. We present cross‐country comparisons of “total available adult hours” under different assumptions about the varying time needs of families of different size. We also present estimates of “time shortages.” In all cases, we provide separate estimates for families located at different points in the country income distributions, since being short of both time and money is likely to be particularly problematic. Although paid work hours are highest for high‐income families, we nonetheless find significant numbers of lower‐income families in which parents work very long hours in the paid labor market; this is particularly the case in the U.S.

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)0.002
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 score0.017
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.015
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.292
Teacher spread0.273 · 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

Citations35
Published2007
Admission routes2
Has abstractyes

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