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Record W4411455185 · doi:10.1177/01492063251342190

Career Success and Minority Status: A Review and Conceptual Framework

2025· review· en· W4411455185 on OpenAlexaff
Mina Beigi, Melika Shirmohammadi, Mostafa Ayoobzadeh, Amir Hedayati Mehdiabadi, Wee Chan Au, Huainan Wang, Qingyang Xu, Yafan Yu, Jane Parry, Ben Whitburn

Bibliographic record

VenueJournal of Management · 2025
Typereview
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsInvisibilityIdentity (music)ScholarshipDiversity (politics)Ethnic groupConceptual frameworkCareer developmentPublic relationsSociologyPsychologySocial psychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

In recent years, the management field has witnessed a surge in studies examining career success among workers from historically marginalized minority groups. However, to date, insights gained from this research remain fragmented and have not been integrated into the existing career success frameworks. We aim to complement career success scholarship and contribute to its inclusivity by conducting a systematic review that synthesizes the factors and pathways contributing to the career success of four historically underrepresented minority groups: women, racial and ethnic minorities, individuals with disabilities, and the LGBTQ+ community. Evidencing that career success disparity can be attributed to minority status, we propose a framework that highlights the career advancement and human and psychological resources associated with minority groups’ career success, as well as the systemic barriers limiting access to and use of such resources. We suggest hypervisibility, invisibility, and managed visibility as distinguishable forms of identity-based mechanisms that offer theoretical explanations for the influence of marginalized identity status on career success. Our framework integrates manifestations of subjective career success—accounting for survival, the collective good, and adjustability in addition to what extant literature has shown—emphasizing that membership in marginalized groups, communities, and other identity-relevant contexts shapes the subjective meaning of career success. Our review has practical implications for decision makers and organizations intending to bridge minority and nonminority groups’ career success disparity.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.150
GPT teacher head0.386
Teacher spread0.235 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2025
Admission routes1
Has abstractyes

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