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Record W2003158254 · doi:10.1525/si.2002.25.3.323

Time, Gender, and the Negotiation of Family Schedules

2002· article· en· W2003158254 on OpenAlexaff
Kerry Daly

Bibliographic record

VenueSymbolic Interaction · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNegotiationControl (management)Variety (cybernetics)Everyday lifeOrchestrationPsychologySociologySocial psychologyPolitical scienceComputer scienceManagementEconomicsSocial science

Abstract

fetched live from OpenAlex

I examine the interactive processes by which women and men negotiate family time schedules. Based on fifty interviews with seventeen dual‐earner couples, I focus on the ways men and women define time in gendered ways, exert different controls over the way time is used, and align their time strategies in the course of managing everyday family life. The results indicate that there are both continuities and discontinuities with the past: women continue to exert more control over the organization of time in families, but time negotiation itself has become a more complex and demanding activity. The way that couples carry out these negotiations reflects a variety of adaptive strategies, with some couples being very reactive in contending with present demands and others being highly structured and seeking to anticipate and control the future. Although some couples worked to negotiate balance in their time responsibilities, it was wives who maintained control over time and, ultimately, the orchestration of family activity.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
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 head0.042
GPT teacher head0.302
Teacher spread0.260 · 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

Citations110
Published2002
Admission routes1
Has abstractyes

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