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Record W2156870913 · doi:10.1177/0164027512446028

An Uneven Burden

2012· article· en· W2156870913 on OpenAlex
Claudia Lahaie, Alison Earle, Jody Heymann

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch on Aging · 2012
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcGill UniversityCarleton University
FundersUniversity of Pittsburgh
KeywordsImmigrationFlexibility (engineering)Ethnic groupPsychologyEducational attainmentGerontologySick leaveWork scheduleScheduleWork (physics)MedicineDemographic economicsPolitical science

Abstract

fetched live from OpenAlex

Using data from a large, nationally representative U.S. survey, the authors examine differences in adult caregiving responsibilities, working conditions, and caregiver outcomes by gender, ethnicity, immigration status, and educational attainment. Women, Hispanics, and first-generation immigrant caregivers are more likely to have worse working conditions including an unsupportive culture at work, less schedule flexibility, and less access to paid vacation leave. Women and first-generation immigrant caregivers are more likely to report caregiving contributing to poor job outcomes including quitting work, retiring early, or reducing hours involuntarily. Caregivers with less than a college degree experience both worse working conditions including lack of access to paid sick days and worse job outcomes including having lost wages or a job. Women, second-generation immigrants, and those with a high school diploma or less are more likely to report that their caregiving responsibilities interfere with their quality of life including negative impacts on their ability to spend time with friends. Research and policy implications are discussed.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.400
GPT teacher head0.625
Teacher spread0.225 · 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