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Record W4406783423 · doi:10.1093/workar/waae023

Exploring midlife identity negotiations in the context of the gender career gap: an interdisciplinary conceptual framework

2025· article· en· W4406783423 on OpenAlexaff
Vanessa Burke, Ho Kwan Cheung, Lisa M. Finkelstein

Bibliographic record

VenueWork Aging and Retirement · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNegotiationIdentity (music)Context (archaeology)Promotion (chess)Psychological interventionConceptual frameworkCareer developmentIdentity negotiationSociologyConceptual modelGender studiesPsychologyPublic relationsSocial psychologyPolitical sciencePoliticsSocial scienceEpistemology

Abstract

fetched live from OpenAlex

Abstract The gender gaps in career outcomes (e.g., pay, promotion, leadership opportunities) observably widen during mid-career, yet research often neglects considerations of gendered age identities in explaining this disparity. The present paper addresses this through an integrative review of interdisciplinary literature and proposes a novel theoretical framework that combines midlife development and gender identity negotiations to better understand mid-career disparities. In this review, we (1) adopt an inter-categorical approach to explore how workers navigate the overlapping systems of gender and age in the workplace, (2) critically review midlife development literature, highlighting significant oversights in organizational research, and (3) we introduce a process model of midlife gendered identity negotiations. We detail the model, describing the antecedents, mechanisms, and outcomes of gendered aging identity negotiations on mid-career inequities. We provide a foundation for advancing research and designing interventions to address gender disparities in mid-career outcomes.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.152
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.313
GPT teacher head0.375
Teacher spread0.062 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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