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Record W1758977782 · doi:10.3233/wor-2011-1202

Care work versus career work: Sibling conflict over getting priorities right

2011· article· en· W1758977782 on OpenAlexaffabout
Bonnie Lashewicz

Bibliographic record

VenueWork · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSiblingWorkforceWork (physics)PsychologyPaid workCare workPopulationWorkloadDemographic economicsSociologyDevelopmental psychologyEconomic growthEconomicsManagementDemography

Abstract

fetched live from OpenAlex

As the average age of the Canadian population continues to increase, and providing care at home to frail older adults becomes ever more prevalent, support for family and friend caregivers remains a key social policy issue. Economic support is an important consideration given the impact of caregiving on labour force participation. Yet the caregiving/paid work relationship is not always straightforward. While caregiving often restricts employment, limited attachment to employment may also influence the decision to provide care. Isabel's story, collected as part of a study of sibling views of fairness in sharing parent care as well as parent assets, provides a case study in how siblings give different priority to care work versus career work and what support needs arise including those related to sibling conflict over differing priorities. Isabel claims she sacrificed her career to care for her ailing mother while her siblings argue that through caregiving, Isabel was sheltered from the paid workforce.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.005
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.286
Teacher spread0.238 · 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 designQualitative
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
Published2011
Admission routes2
Has abstractyes

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