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Record W4388980754 · doi:10.1080/13668803.2023.2275975

Work-family justice – meanings and possibilities: introduction to the work and family researchers network special issue

2023· article· en· W4388980754 on OpenAlexaff
Melissa A. Milkie, Heejung Chung, Ameeta Jaga

Bibliographic record

VenueCommunity Work & Family · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScholarshipWork (physics)Economic JusticeSociologyEngineering ethicsPoliticsPublic relationsPolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

Work-Family Justice is a key organizing concept centering intellectual and policy work that call attention to tensions and challenges in work and family integration, and that highlight key solutions. This special issue extends knowledge about structural, cultural, historical, and political (including geopolitical) oppressions that inform the range of diverse work-family conflict complexities and presents building blocks to sustain healthier work and family lives. Work-family justice allows for safe, decent, and meaningful work, supported care for dependents, and strong family relations though the life course. It addresses inequalities between and across groups and cultures. We build upon earlier rigorous scholarship ascertaining the best supports for a healthy and fulfilled workforce and populace, which can advance equality and profit national wellbeing. The special issue highlights exceptional individual research studies, that -- as a whole -- elevates work-family scholarship and the solutions that can enhance work-family justice.

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)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0080.009
Scholarly communication0.0110.015
Open science0.0030.012
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0170.005

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 head0.086
GPT teacher head0.339
Teacher spread0.252 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations6
Published2023
Admission routes1
Has abstractyes

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