Exploring midlife identity negotiations in the context of the gender career gap: an interdisciplinary conceptual framework
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".