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Family Ties: Constructing Family Time in Low‐Income Families

2005· article· en· W2031446653 on OpenAlexaff
Carolyn Y. Tubbs, Kevin Roy, Linda M. Burton

Bibliographic record

VenueFamily Process · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCentralityPovertyConstruct (python library)WelfareFamily tiesPsychologyMeaning (existential)EthnographyDevelopmental psychologyDemographic economicsSocial psychologyLongitudinal studySociologyEconomic growthEconomicsMedicineGenealogy

Abstract

fetched live from OpenAlex

"Family time" is reflected in the process of building and fortifying family relationships. Whereas such time, free of obligatory work, school, and family maintenance activities, is purchased by many families using discretionary income, we explore how low-income mothers make time for and give meaning to focused engagement and relationship development with their children within time constraints idiosyncratic to being poor and relying on welfare. Longitudinal ethnographic data from 61 low-income African American, European American, and Latina American mothers were analyzed to understand how mothers construct family time during daily activities such as talking, play, and meals. We also identify unique cultural factors that shape family time for low-income families, such as changing temporal orientations, centrality of television time, and emotional burdens due to poverty. Implications for family therapy are also 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.

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)0.005
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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0030.002
Open science0.0010.003
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 head0.020
GPT teacher head0.294
Teacher spread0.274 · 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

Citations98
Published2005
Admission routes1
Has abstractyes

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