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Record W2974693711 · doi:10.5465/amd.2018.0105

Work–Life Balance as a Household Negotiation: A New Perspective from Rural India

2019· article· en· W2974693711 on OpenAlex
Rachael D. Goodman, Sarah Kaplan

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

VenueAcademy of Management Discoveries · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWork (physics)NegotiationWorkforceBalance (ability)Work–life balancePerspective (graphical)Economic growthEconomicsSociologyLabour economicsBusinessPsychologySocial science

Abstract

fetched live from OpenAlex

Bringing more women into the formal workforce is an important component of corporate strategies, development efforts, and the United Nation’s Sustainable Development Goals. Yet, these policies often do not consider the household work that women already do to support the survival of their families, making work-for-wages impossible or creating time famines for women who attempt to do both. Thus, for women to engage in work-for-wages, they must find a way to alleviate their work-for-households. Using the analytical lens of household decision-making from anthropology, our analysis of working women in rural India shows that, far from being an individual decision about time allocation, women’s ability to work formal jobs was a family project to reallocate labor. These insights suggest that the focus on the individual in work–life balance literature and policy-making inadequately represents a phenomenon that involves other household members, implicitly or explicitly. It also highlights the need to broaden our definition of “work” to include both the paid and unpaid labor that is vital to people’s survival.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.284
Teacher spread0.262 · 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