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Record W2604539924 · doi:10.3138/jcfs.41.1.19

Is Justice Contextual? Married Women’s Perceptions of Fairness of the Division of Household Labor in 12 Nations

2010· article· en· W2604539924 on OpenAlexvenueno aff
Shannon N. Davis

Bibliographic record

VenueJournal of Comparative Family Studies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDivision of labourDistributive justicePoliticsContext (archaeology)NormativeEconomic JusticeEconomicsSociologySocial psychologyDemographic economicsPsychologyPolitical scienceLawMicroeconomics

Abstract

fetched live from OpenAlex

Distributive justice theory argues that individuals make fairness decisions partly by comparing themselves to similarly situated others. Utilizing fixed effects analyses of 4,643 married women nested in 12 nations from the l 991 International Social Justice Project, this paper examines whether the relationship between the division of household labor and perceptions of fairness of that division was informed by such comparisons. Specifically, this paper tests whether political history, women’s political representation, and average division of labor within a nation set expectations for the division of household labor against which married women made fairness determinations. Although political history and women’s political representation seem to have been directly influential in constructing fairness determinations in 199 1, married women did not seem to use the expectations of (in)equality as comparison referents. The normative division of labor within a nation did not directly or indirectly influence perceptions of fairness. Nations provide a context within which married women make fairness determinations; setting expectations does not seem to have been the mechanism through which context has historically operated. Additional cross-national research on this topic is warranted.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.372
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2010
Admission routes1
Has abstractyes

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